MétaCan
Menu
Back to cohort
Record W4285731491 · doi:10.1111/joac.12503

(Landlord) Theory from the South: Empire and estates on a Punjabi Frontier

2022· article· en· W4285731491 on OpenAlexafffund
Shozab Raza

Bibliographic record

VenueJournal of Agrarian Change · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsUniversity of Toronto
FundersJackman Humanities Institute, University of TorontoInternational Development Research CentreWenner-Gren Foundation
KeywordsColonialismAgrarian societyFrontierScholarshipEmpireSociologyModernization theoryHegemonyLandlordPolitical economyLawPolitical scienceAgriculturePoliticsHistory

Abstract

fetched live from OpenAlex

Abstract Theory has occasionally shaped agrarian transformations. Utilitarian theory, for instance, influenced British colonial land revenue policies, while modernization theory spurred, via the Green Revolution, the development of capitalist farming across the global South. Yet scholarship, when it has probed the mediation of theory in agrarian change, has largely centred on the intellectual activities of Western figures. In this paper, I examine an under‐appreciated theorizing actor: landlords in the global South. I explore landlords' concept‐work in the former “Punjab Frontier,” a region where Baloch chiefs collaborated with the British Raj to acquire localized magisterial powers, a paramilitary apparatus, and immense “landed estates” (jagirs). To overcome various crises, certain chiefs engaged with various imperial concepts—namely, property, race, progress, contract, and freedom—and re‐arranged their estates. By showing how these elites creatively embraced these concepts to maintain a colonial‐fortified hegemony, I also challenge those who overstate the emancipatory and decolonial possibilities of theory from the South.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.023
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.198
Teacher spread0.165 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Agrarian ChangeSame topicAgriculture, Land Use, Rural DevelopmentFrench-language works237,207