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Record W4309759175 · doi:10.1080/10455752.2022.2144397

Reifications in Disease Ecology 1: Demystifying Land Use Change in Pathogen Emergence

2022· article· en· W4309759175 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCapitalism Nature Socialism · 2022
Typearticle
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDilemmaEnvironmental ethicsEcologyPolitical ecologyNarrativeSociologyIndigenousDialecticEpistemologyBiologyPolitical science

Abstract

fetched live from OpenAlex

Disease ecology has the potential to help build a new society where the contradictions of our time are recognized and confronted in the pursuit of a more considered, and just, understanding of the interrelationships of organisms with the environment. Unfortunately, the discipline is facing a major dilemma as the advent of new technologies, access to remote data, and lack of engagement with the contexts where diseases emerge and are transmitted, has resulted in the creation of Blame Local Indigenous and Peasant Populations (BLIPP) narratives that align with hegemonic globalizing agents and processes. Here, in the first half of a two-part essay about reifications in disease ecology, thinking with dialectical materialism, we demystify BLIPP narratives around land use change in disease emergence.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.344
Teacher spread0.297 · 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