diff --git a/experimental/data_journeys/README.md b/experimental/data_journeys/README.md new file mode 100644 index 000000000..b87748a77 --- /dev/null +++ b/experimental/data_journeys/README.md @@ -0,0 +1,7 @@ +Cases for the data journeys presentation/paper. + +Originally prepared by simonf@ for AAG 2026 + +* [How the "Missing Survey" Drives India's Demolition Crisis](india_slum_demolition_survey_absence.md) +* [Spain SIGPAC](spain_sigpac_error_correction.md) +* [Malawi: Africa RiskView "No Trigger" Crisis (2015/16)](malawi_arc_no_trigger.md) diff --git a/experimental/data_journeys/india_slum_demolition_survey_absence.md b/experimental/data_journeys/india_slum_demolition_survey_absence.md new file mode 100644 index 000000000..52301f8d5 --- /dev/null +++ b/experimental/data_journeys/india_slum_demolition_survey_absence.md @@ -0,0 +1,154 @@ +# The Cartography of Erasure: How the "Missing Survey" Drives India's Demolition Crisis + +In the rapidly urbanizing landscapes of Delhi and Mumbai, the access to +rehabilitation after slum evictions has become contingent on a specific +geospatial record: the government survey. The state procures satellite maps +every three months to detect new construction for demolition — demonstrating +high geospatial capacity for enforcement. + +Eligibility for rehabilitation depends on a different instrument entirely: a +joint survey and documentary proof under the 2015 +[rehabilitation policy](https://delhishelterboard.in/main/wp-content/uploads/2019/01/Relocation-Policy-2015.pdf). +We see two data journeys on the same territory: one completes, the other one is +blocked. + +Courts have [repeatedly](https://indiankanoon.org/doc/159570569/) +[emphasised](https://indiankanoon.org/doc/39539866/) that a detailed survey +prior to eviction and a rehabilitation plan are essential to due process; in +practice, disputes often centre on whether a meaningful survey happened and who +was counted. + +In 2023 alone, approximately **280,000 people** were forcibly +[evicted](https://tribe.article-14.com/post/2-8-lakh-homes-demolished-in-delhi-in-2023-but-rehab-fails-as-bjp-aap-ignore-their-own-promises-court-orders-66442cca27158) +in Delhi, the +[highest](https://www.newsclick.in/more-half-million-people-5-lakh-evicted-india-2023-report) +number in India according to the Housing and Land Rights Network (HLRN) . Yet, +rehabilitation often hinges on two gatekeepers: (1) whether a cluster is treated +as ‘protected/eligible’ under the policy framework (officially limited to 675 +recognized *Jhuggi Jhopri* or JJ clusters) and (2) whether households meet the +policy’s cut-off dates, possess correct documentation, and appear in a +[joint survey](https://www.newslaundry.com/2025/06/30/delhis-demolition-drive-27000-displaced-from-9-acres-of-encroached-land). +Enforcement tends to be well-instrumented (e.g., satellite monitoring aimed at +spotting ‘new’ jhuggis), while rehabilitation turns on slower, contested +steps—joint surveys, voter-list appearance, and pre-2015 documents—where +exclusions are common. + +A June 2025 +[ground report](https://www.newslaundry.com/2025/06/30/delhis-demolition-drive-27000-displaced-from-9-acres-of-encroached-land) +assessed 27,000+ people displaced across multiple demolition drives since the +BJP came to power; it also reports that 344 homes (about five acres) were razed +at Bhoomiheen Camp on June 11. In Mumbai, despite a slum population exceeding 5 +million (42% of the city), Wilson Center reporting +[estimates](https://www.wilsoncenter.org/article/building-slum-free-mumbai) +about 0.15 million tenements rehabilitated under the model over two decades +(with ~0.12 million approved but not begun), against an early target of 1 +million. + +## What the State Could See — and What It Chose Not to Count + +### Selective Mapping + +The state demonstrates high-resolution precision when mapping land for +infrastructure projects—metro lines, highways, and commercial complexes. This +"structure mapping" facilitates clearance. Conversely, "social mapping"—the +enumeration of families, tenure history, and eligibility—is systematically +neglected. + +The landmark *Sudama Singh* [judgment](https://indiankanoon.org/doc/39539866/) +(2010) of the Delhi High Court mandated that *prior* to any eviction, the state +must conduct a survey of all persons to determine eligibility. In practice, +agencies circumvent this by declaring settlements as "new encroachments" not +found on the official list, thereby bypassing the obligation to survey or +rehabilitate. + +### The Survey Gap: 6,343 vs. 675 + +The magnitude of the data deficit is stark. The National Sample Survey Office +(NSSO) 69th Round (2012) +[identified](https://des.delhi.gov.in/sites/default/files/urban_slums_in_delhi.pdf) +**6,343 slums** in Delhi, housing over a million households. However, the Delhi +Urban Shelter Improvement Board (DUSIB) officially recognizes only **675 JJ +Bastis** (clusters) for rehabilitation purposes. These figures aren’t perfectly +like-for-like (NSS uses a broad ‘slum’ definition; ‘JJ clusters’ are an +administrative category), but the comparison still illustrates a major gap +between the estimated universe of informal settlements and the subset treated as +protected/eligible in practice. + +This discrepancy means that nearly 90% of Delhi's slum settlements exist in a +legal blind spot. Residents of these "non-notified" slums are vulnerable to +summary eviction because their settlements do not appear on the JJ Bastis list +used by policymakers. Slum coverage is +[poorly organized](https://timesofindia.indiatimes.com/city/delhi/ud-department-seeks-information-on-82-slum-clusters-gone-missing-in-delhi/articleshow/99222772.cms), +with documents often unavailable. + +## How Absence Became Ineligibility + +### The "Cut-Off" Date Trap + +Eligibility for rehabilitation is anchored to arbitrary chronological +milestones. In Delhi, the *Delhi Slum & JJ Rehabilitation and Relocation Policy, +2015* stipulates a dual requirement: the slum cluster must have existed prior to +**January 1, 2006**, and the individual family must possess documents proving +residence prior to **January 1, 2015**. + +In Mumbai, the exclusionary mechanics are even more stratified. Residents are +entitled to free housing only if they can prove residence prior to **January 1, +2000**. Those arriving between 2000 and 2011 fall into a "paid rehabilitation" +tier, requiring them to pay +[₹2.5 lakh](https://citizenmatters.in/no-more-free-rehousing-but-government-offers-discounted-homes-to-slum-dwellers/) +(with some early proposals pushing even higher) for a replacement tenement—a sum +often prohibitive for the urban poor. Structures outside those windows are +ineligible. + +The state's failure to update these maps creates a "relocation gap." Families +displaced from surveyed areas often move to unsurveyed peripheral lands, where +they are again classified as "new encroachers," +[trapping](https://www.wilsoncenter.org/article/building-slum-free-mumbai) them +in a cycle of perpetual displacement. + +### Judicial Ambivalence + +The judiciary, once the guardian of housing rights, has increasingly +[facilitated](https://article-14.com/post/how-delhi-s-govt-is-using-its-slum-rehabilitation-policy-to-deny-rehabilitation-to-those-who-need-it-6858cb6129469) +this erasure. While *Sudama Singh* (2010) and *Ajay Maken* (2019) established +robust protections, recent rulings have diluted them. A 2022 Delhi High Court +[ruling](https://indiankanoon.org/doc/131330551/) interpreted the 2015 Policy +restrictively, effectively +[denying](https://scroll.in/article/1053522/how-a-delhi-high-court-judgement-has-made-slum-residents-vulnerable-to-arbitrary-evictions) +relief to residents of non-notified slums because they were not on the DUSIB +list. + +This jurisprudence effectively treats the "list" as conclusive proof of +existence, ignoring the reality that the list itself is outdated and incomplete. +The courts now frequently treat the absence of a survey as the resident's +failure rather than the state's breach of duty. + +### The Right to be Counted + +The "geospatial void" is a political choice. Slum dweller organizations argue +that the refusal to survey is a refusal to recognize citizenship. Residents are +currently +[demanding](https://cpiml.org/mlupdate/slum-dwellers-convention-demands-permanent-halt-on-bulldozer-rampage-in-delhi) +a fresh survey of all JJ clusters, with a revised cut-off date of **July 1, +2025**, to reflect the reality of post-2015 urbanization. + +Without a survey, a family that has lived in a *jhuggi* for twenty years—paying +for electricity and voting in elections—is legally indistinguishable from a +squatter who arrived yesterday. + +## See also + +[Policy Analyses and Recommendations for the Delhi Urban Shelter Improvement +Board](https://spia.princeton.edu/sites/default/files/content/India_Policy_Workshop_Report-Final.pdf) +from the Woodrow Wilson School + +## Framework annotation + +* blockade->void: enforcement data journey completes while recognition data + journey is strategically blocked; governing effect is non-existence — + families with decades of residence rendered invisible +* the cut-off dates and list requirements launder the void through gate-like + procedural language ("no survey, no list, no entitlement") +* courts in Sudama Singh and Ajay Maken were reaching for something like a + warrant card — demanding the state declare what it knows before it + demolishes. diff --git a/experimental/data_journeys/malawi_arc_no_trigger.md b/experimental/data_journeys/malawi_arc_no_trigger.md new file mode 100644 index 000000000..6719b6fa3 --- /dev/null +++ b/experimental/data_journeys/malawi_arc_no_trigger.md @@ -0,0 +1,153 @@ +# Malawi: Africa RiskView "No Trigger" Crisis (2015/16) + +In the 2015/16 agricultural season, Malawi declared a national state of disaster +as drought left 6.5 to 6.7 million people +[food-insecure](https://actionaid.org/publications/2017/wrong-model-resilience). +The government had purchased drought insurance costing approximately $4.7 +million (often rounded to $5 million) from the African Risk Capacity (ARC), a +G7-backed parametric insurance mechanism. ARC is designed to provide rapid +payouts —typically within +[2-4 weeks](https://www.arc.int/sites/default/files/2021-09/XCF-Policy-Brief-Summary.pdf))— +when satellite-detected rainfall deficits lead to estimated response costs +[exceeding](https://www.arc.int/how-arc-works) specified thresholds. + +The insurance did not pay out immediately. The satellite model +[calculated](https://www.researchgate.net/publication/322357484_The_wrong_model_for_resilience_How_G7-backed_drought_insurance_failed_Malawi_and_what_we_must_learn_from_it) +that only **20,594** people had been affected—a figure over three hundred times +lower than reality. The model had been calibrated for a crop that farmers no +longer grew. + +## What the Index Measured — and What It Missed + +### The Water Requirements Satisfaction Index + +Africa RiskView, ARC's technical engine, uses the Water Requirements +Satisfaction Index (WRSI)—a [model](https://www.arc.int/drought) developed by +the FAO in the 1970s that monitors water deficits throughout the growing season. +The model takes satellite-based rainfall estimates and +[calculates](https://africariskview.org/Content/Technical-Note_en.pdf) the ratio +of actual to potential evapotranspiration: how much water the crop received +versus how much it needed. + +WRSI translates rainfall into crop stress, and crop stress into estimated +affected populations. When the index crosses a predetermined threshold, +insurance payouts are triggered automatically—no claims process, no ground +verification, no delay. This is the promise of parametric insurance: speed at +scale. + +But WRSI requires accurate parameterization. The model must know what crop is +being grown, when it was planted, how long its growing season lasts, and what +its water requirements are at each growth stage. Get these parameters wrong, and +the model monitors a phantom crop while the real one dies. + +### The Phenology Mismatch + +Malawi's ARC policy was +[calibrated](https://www.arc.int/sites/default/files/2021-10/Malawi-Response-Matrix-Clean.pdf) +assuming farmers primarily planted late-maturing maize varieties with 120-140 +day growing cycles. This assumption was embedded in the model's "crop +calendar"—the temporal window during which rainfall deficits would be monitored. + +But Malawian farmers, responding to shifting climate patterns and extension +advice about drought risk, had increasingly switched to early-maturing varieties +with 90-day cycles. These shorter-season crops mature faster, requiring rain at +different times than their longer-season cousins. + +The 2015/16 rainfall pattern was particularly cruel to short-cycle maize. Dry +spells hit precisely during the critical growth phases of the 90-day varieties. +But the model, looking for deficits in the 120-140 day window, recorded a +"passable" season. The long-cycle maize the model tracked would have survived; +the short-cycle maize that farmers actually planted did not. + +### The Trigger That Didn't Trigger + +When rainfall becomes low enough, the WRSI value drops. If this agronomic index +falls below a critical threshold during the monitored window, the model +estimates a high affected population and a correspondingly high response cost, +triggering the insurance. In 2015/16, Malawi's WRSI—calculated for the wrong +crop—stayed above this critical threshold. The model said: minor drought, 20,594 +affected. The government said: national emergency, 6.7 million affected. + +## How the Proxy Became the Decision + +### The $4.7 Million Premium, $300 Million Gap + +Malawi paid ~$4.7 million in insurance premiums for coverage that was supposed +to address drought. The eventual $8.1 million payout—itself contested as +inadequate—arrived against a backdrop of a humanitarian funding gap estimated at +over $300 million (total response costs were ~$395 million). ActionAid's +analysis concluded the payment was "too little, too late" and "effectively +represented an economic loss to Malawi". + +The arithmetic was brutal: Millions paid for insurance that didn't trigger when +needed, forcing the government to seek emergency humanitarian funding anyway, +then receiving a small payout nine months later that couldn't address the +immediate crisis it was meant to prevent. + +For ActionAid and local civil society, the failure highlighted a severe +opportunity cost: the $4.7 million premium could have funded decentralized +weather stations, irrigation, or the Village Savings and Loans (VSLs) networks +that rural Malawian women actually relied on during crises. Government officials +indicated they would not renew the policy. + +### The Epistemological Problem + +Parametric insurance depends on the premise that satellite observations can +substitute for ground truth — that the model's assessment is close enough to +reality that automated triggers are preferable to slow, expensive claims +verification. The Malawi case illustrates what happens when that premise fails. + +The problem wasn't that satellites can't measure rainfall accurately. The +problem was that the chain from rainfall to crop stress to food insecurity +requires knowledge that satellites cannot provide: what farmers actually +planted, when they planted it, and how their specific varieties respond to +specific weather patterns. This "ground truth" about cropping patterns was +outdated, and the model had no mechanism to detect its own obsolescence. + +### The Retrospective Recalculation + +In its own defense, ARC maintained that the system's safeguards functioned as +intended. When discrepancies emerged between the model's output and the ground +truth reported by the Malawi Vulnerability Assessment Committee (MVAC), ARC's +framework allowed for a technical review. After months of dispute and field +investigations, ARC recalculated the model using the corrected 90-day maize +parameters. The revised index showed severe drought impact—triggering the +payout. + +In November 2016, ARC announced an $8.1 million payout. By January 2017, when +the funds finally arrived, the humanitarian response had been funded through +other means, the planting season had passed, and the "rapid" in "rapid response" +had failed to materialize. What was designed as 2-4 week emergency financing +arrived nine months after the disaster declaration. + +### The Legitimacy Collapse + +The Malawian government formally disputed ARC's "satellite truth," forcing an +expensive retrospective ground audit that ARC's model was designed to avoid. +Kenya and Malawi +[pulled out](https://www.globalinclusiveinsuranceforum.org/sites/default/files/The-Future-of-Disaster-Risk-Pooling-for-Developing-Countries.pdf) +of ARC for the 2016-17 season. Participation across the continent plummeted: +just three countries (Burkina Faso, Senegal, The Gambia) took part in the +2018/19 pool. The model's failure in one country contaminated confidence across +the continent. + +## The Aftermath: Evolution and Return + +The 2015/16 crisis was +[studied](https://www.researchgate.net/publication/322357484_The_wrong_model_for_resilience_How_G7-backed_drought_insurance_failed_Malawi_and_what_we_must_learn_from_it) +and +[catalyzed](https://www.arc.int/sites/default/files/2021-10/ARC_LessonsLearned.pdf) +significant reforms: ARC instituted mandatory customization reviews, sensitivity +analyses, and strengthened in-country Technical Working Groups. Malawi +eventually rejoined the risk pool, and during the 2021/2022 season the updated +system successfully triggered a $14.2 million payout — proving the model could +learn. + +## Framework annotation + +* path->gate: linear travel from satellite to model to reinsurer to trigger; + binary threshold determination +* asymmetric uncertainty burden: model permitted to guess at national scale; + government required to prove it wrong at field level, retroactively, at own + expense +* this sits on the diagonal diff --git a/experimental/data_journeys/spain_sigpac_error_correction.md b/experimental/data_journeys/spain_sigpac_error_correction.md new file mode 100644 index 000000000..33b803565 --- /dev/null +++ b/experimental/data_journeys/spain_sigpac_error_correction.md @@ -0,0 +1,213 @@ +# Spain SIGPAC: Governance by Workaround + +In July 2025, Spain's Supreme Court ruled that farmers are liable for errors in +the state's own agricultural map (unless they can prove they flagged the +mismatch on time and it wasn’t corrected for reasons not attributable to them). +By submitting the annual subsidy application, the farmer accepts the geospatial +data as correct — even when the error originated in the state's classification +algorithm (SIGPAC) or other sources. The court explicitly rejected the argument +that this imposed an excessive burden on applicants who lack the technical means +to verify complex geodetic accuracy. The institution makes the map. The +institution makes the errors. The farmer must prove they flagged the discrepancy +— or pay for both. + +The ruling codified what the system had already produced in practice. In Spain, +as across the European Union, area-based agricultural support is mediated by +maps. SIGPAC (*Sistema de Información Geográfica de Parcelas Agrícolas*) is the +reference layer used to locate and characterize land for surface-linked payments +and controls, as FEGA +[sets out](https://www.fega.gob.es/es/pepac-2023-2027/sistemas-gestion-y-control/sigpac). +But the map is permanently imperfect — misclassifying pasture as scrub, fallow +as unproductive, productive land as wasteland. Rather than treating those +imperfections as a centrally owned data-quality problem, the system outsources +error correction to farmers through a formal *alegación* (allegation) process, +making them responsible for identifying, documenting, and proving errors parcel +by parcel. + +## What the Algorithm Sees — and What It Cannot + +SIGPAC is built as a national GIS database grounded in orthophotography and +cadastral geometry, worked at a 1:5,000 scale, as a FEGA training deck hosted by +MITECO +[describes](https://www.miteco.gob.es/content/dam/miteco/es/biodiversidad/formacion/sigpac_tcm30-429772.pdf). +A parcel — and especially the *recinto*, the internal reference unit — receives +a land-use label and attributes that become "real" in the administrative sense. + +Even when the imagery is sharp, the underlying object is unstable. A fallow +field can become scrubby; pasture is grazed unevenly; agroforestry sits +awkwardly between "pasture" and "forest." A photograph captures a moment; a farm +runs on seasons. + +At the EU level, the European Court of Auditors has pointed to the limits of +photo-interpretation for eligibility layers. In its LPIS audit, the Court +[notes](https://www.eca.europa.eu/lists/ecadocuments/sr16_25/sr_lpis_en.pdf) +that photo-interpretation "was not always reliable," contributing to incorrect +maximum eligible areas in some implementations. That report is largely about +over-eligibility errors (too much land deemed eligible). The mirror-image error +— eligible land recorded as less eligible than it is — creates a quieter +problem: income loss that does not produce the same level of administrative +alarm, because it looks like "compliance." + +### Pasture as a Computed Surface + +Pasture is the stress test. Mediterranean grazing areas are often a mix of +herbaceous cover, shrub, scattered trees, rock, slope, and seasonal dynamics. +Spain historically applied the *Coeficiente de Admisibilidad de Pastos* (CAP) +and, from 2023, moved to the *Coeficiente de Subvencionabilidad de Pastos* +(CSP), as FEGA +[summarizes](https://www.fega.gob.es/es/node/8079/printable/print). + +CSP is a computed percentage: the share of a pasture parcel deemed eligible +after discounting what the algorithm treats as limiting — excessive slope, bare +ground, non-pasturable shrub, tree cover, buildings +([FEGA CSP note](https://www.fega.gob.es/sites/default/files/files/document/230302_nota_web_csp_2023.pdf)). +The eligible area becomes **total area × CSP**, not the cadastral surface. + +The CSP 2023 coefficient is built through automatic processes using information +captured by satellites and aircraft, producing cartographies that represent +limiting factors. The core inputs are satellite imagery (including +multi-spectral HR/VHR) for identifying non-vegetated areas, terrain models for +slope, and LiDAR flights to discriminate vegetation by height and structure +([FEGA CSP note](https://www.fega.gob.es/sites/default/files/files/document/230302_nota_web_csp_2023.pdf)). +A regional technical annex describes CSP as calculated on a **5×5 m pixel grid** +and then intersected with SIGPAC *recintos* to derive the eligible surface in +each pasture unit +([Extremadura CSP annex](https://www.juntaex.es/documents/77055/1878068/ANEXO%2BCOEFICIENTE%2BSUBVENCIONALIDAD%2BDE%2BPASTOS%2B%28CSP%29.pdf/fc1dc941-4203-dd13-14c3-60d389c7fd76?t=1699968238972)). + +Two consequences follow. First, errors are now discussed as numbers, not as +lines on the map: the boundary of the *recinto* can be correct while the +coefficient reduces the eligible surface inside it. Contesting the error means +contesting how pixels were classified — rock versus bare soil, shrub versus +pasture, tree structure versus canopy gap — rather than where the polygon sits. +Second, the coefficient incorporates a "species factor" (*factor especie*) that +weights the vegetation factor according to local ecological conditions +([Extremadura CSP annex](https://www.juntaex.es/documents/77055/1878068/ANEXO%2BCOEFICIENTE%2BSUBVENCIONALIDAD%2BDE%2BPASTOS%2B%28CSP%29.pdf/fc1dc941-4203-dd13-14c3-60d389c7fd76?t=1699968238972)). +A pasture with scattered trees or scrub may be treated as partly ineligible on +paper, even when it is grazed in full. CSP is not just "what the satellite saw," +but how institutions decide to treat what it saw. + +## How the Classification Became a Liability + +The responsibility allocation is explicit. FEGA +[frames](https://www.fega.gob.es/es/pepac-2023-2027/sistemas-gestion-y-control/sigpac) +the beneficiary as the "ultimate" responsible party for ensuring that the SIGPAC +information used in the application matches reality, and +[directs](https://www.fega.gob.es/es/content/%C2%BFc%C3%B3mo-se-debe-actuar-ante-un-error-de-la-informaci%C3%B3n-o-la-delimitaci%C3%B3n-de-un-recinto-del) +discrepancies into the allegation procedure. *Real Decreto 1048/2022* — the +royal decree establishing rules for applying CAP strategic plan interventions in +Spain — [states](https://www.boe.es/buscar/act.php?id=BOE-A-2022-23048) that the +farmer must verify that the CSP assigned to their parcels corresponds to the +land's reality. The system produces an official surface, then instructs the +beneficiary to treat that surface as something they must continuously verify, +correct, and document against. + +The Supreme Court ruling of July 2025 +([STS 3761/2025, resolution 1097/2025](https://www.poderjudicial.es/search/TS/openDocument/b01b2a42354a979fa0a8778d75e36f0d/20250807)) +completed this logic. It explicitly overturned the High Court of Justice of +Aragon (STSJ Aragón, sentencia 148/2022, de 17 de marzo, recurso 12/2020), which +had ruled that the farmer neither has nor can be expected to have the technical +means to determine whether the admissibility coefficient is correct, and that +the duty to maintain an accurate SIGPAC falls on the state, not the applicant. +The Aragon court had found that the farmer 'does not have, nor can be required +to have, the adequate technical means to know whether the admissibility +coefficient applied to their aid application is correct' (no tiene, ni es +exigible que lo tenga los medios técnicos adecuados) — a finding the Supreme +Court set aside. + +Surface-linked subsidy systems reinforce this asymmetry by design: over-claiming +is treated as risk and triggers penalties, while under-claiming is treated as +safe. The allegation workflow does not only correct errors. It disciplines +behavior: when the map is uncertain, it is safer to accept the database's +smaller number than to contest it under deadline pressure. Farmers comply with +the map not because it is accurate, but because contesting it is more expensive +than absorbing the loss. The system governs through the cost of correction, not +the accuracy of classification. + +## How the Circuit Closes + +Alongside SIGPAC as a reference layer, EU control practice has moved toward +continuous satellite-based monitoring. FEGA's monitoring circular +[defines](https://www.fega.gob.es/sites/default/files/files/document/AD_Circular_40-2024_EE107303_PN_controles_monitorizacion.pdf) +a traffic-light logic — **green** (compliant), **yellow** (inconclusive), +**red** (non-compliant) — and sets out how follow-up evidence is requested and +assessed. The same circular +[sets](https://www.fega.gob.es/sites/default/files/files/document/AD_Circular_40-2024_EE107303_PN_controles_monitorizacion.pdf) +a general deadline for additional evidence and, for monitored interventions, +adaptation of parcels in the *Solicitud Única* up to **31 August**. This is not +the SIGPAC-modification deadline as such, but it is a monitoring and evidence +window that shapes how quickly farmers must document field reality. + +The result is a feedback structure. The algorithm classifies. The farmer +responds — by accepting, correcting, or adjusting their declaration. The +monitoring system re-evaluates. New flags generate new evidence demands. Each +cycle produces a new determination that the farmer must either accept or +contest. + +### The Perpetual Beta + +When pasture coefficients are recalculated as part of a new methodology cycle, +the baseline shifts. Extremadura's CSP annex +[introduces](https://www.juntaex.es/documents/77055/1878068/ANEXO%2BCOEFICIENTE%2BSUBVENCIONALIDAD%2BDE%2BPASTOS%2B%28CSP%29.pdf/fc1dc941-4203-dd13-14c3-60d389c7fd76?t=1699968238972) +CSP 2023 as a coordinated update effort, and FEGA +[presents](https://www.fega.gob.es/es/pepac-2023-2027/sistemas-gestion-y-control/sigpac) +it as a replacement for the prior CAP coefficient. Earlier corrections do not +necessarily revert automatically. But when the methodology changes, the baseline +shifts — and corrections made under the old system may no longer apply. What was +settled becomes unsettled. The farmer who proved the algorithm wrong last year +may need to prove it wrong again this year, against a different algorithm, using +different evidence. The map is never finished; the burden never ends. + +### The Cost of Contestation + +When SIGPAC does not match the field, the formal instruction is straightforward: +file an allegation with the competent body in the autonomous community where the +*recinto* sits, as FEGA +[states](https://www.fega.gob.es/es/content/%C2%BFc%C3%B3mo-se-debe-actuar-ante-un-error-de-la-informaci%C3%B3n-o-la-delimitaci%C3%B3n-de-un-recinto-del). +In practice, each allegation is a small administrative project: + +- **Georeferenced photographs.** For CSP-related challenges, Extremadura's + annex + [requires](https://www.juntaex.es/documents/77055/1878068/ANEXO%2BCOEFICIENTE%2BSUBVENCIONALIDAD%2BDE%2BPASTOS%2B%28CSP%29.pdf/fc1dc941-4203-dd13-14c3-60d389c7fd76?t=1699968238972) + *fotografías georreferenciadas* taken with approved photo tooling (SGAapp or + regional equivalents). + +- **Technical reports.** The same annex + [describes](https://www.juntaex.es/documents/77055/1878068/ANEXO%2BCOEFICIENTE%2BSUBVENCIONALIDAD%2BDE%2BPASTOS%2B%28CSP%29.pdf/fc1dc941-4203-dd13-14c3-60d389c7fd76?t=1699968238972) + an *informe técnico* with required minimum contents as part of requesting a + revised coefficient. + +- **Timing constraints.** The Generalitat Valenciana + [states](https://www.gva.es/es/inicio/procedimientos?id_proc=G15401) that + requests can be submitted year-round (default effect: the following year), + but to have effect in the current year they must be filed within the same + deadline as aid applications. + +- **Resolution lag and administrative silence.** Extremadura's SIGPAC + allegation procedure + [lists](https://www.juntaex.es/w/5698?inheritRedirect=true) a six-month + resolution period and specifies *silencio administrativo desestimatorio* — + if the administration does not respond within six months, the request is + deemed denied. + +Each allegation requires comfort with portals, layers, and basic GIS logic; +access to approved photo apps capable of producing georeferenced evidence +meeting quality expectations described in FEGA's monitoring guidance +([circular](https://www.fega.gob.es/sites/default/files/files/document/AD_Circular_40-2024_EE107303_PN_controles_monitorizacion.pdf)); +understanding of CSP component factors; and, in some cases, a paid technical +report. For large operations, this is overhead. For small farmers, the value of +correcting the map may be lower than the cost of doing the correction properly. +The practical consequence during application season is a familiar dilemma: +declare what the database says (accepting a potentially lower payment), or +declare what you actually farm (risking a mismatch against the official layer, +with the Supreme Court having confirmed who bears the risk). + +## Framework annotation: + +* circuit->gate: classification→response→monitoring re-evaluation->new flags; + recurring threshold determinations the farmer must accept or contest +* the Supreme Court's codification of responsabilidad última makes the + asymmetric uncertainty burden not just administratively practiced but + judicially enforceable. +* the off-diagonal placement: same circuit topology as PredPol but gate + warrant rather than loop — the system manufactures compliance burden, not + false evidence)