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Record W6939214235 · doi:10.6084/m9.figshare.12587459

Strengthening Farmers Resilience and Rural Food System in Response to the Pandemic

2020· article· en· W6939214235 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFigshare · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Development and Management
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Psychological resilienceQuarter (Canadian coin)PandemicFood securityRural areaResilience (materials science)Agriculture

Abstract

fetched live from OpenAlex

The economic slowdown due to Covid-19 hits hard in many countries. In Indonesia, the economic growth rate for the first quarter of 2020 is only 2.97%. Simulations estimated the number of unemployed people would rise between 3.5 to 8.5 million, depends on the rate of economic growth. Yet the news often mentioned that these social aids had not reached its targets even in the city's neighborhood. The situation is more uncertain for the rural area. Limited information is available about the pandemic situation in Indonesian rural life. The general assumption prevails that rural life dynamic is different from the urban that results in a lower infection rate. Naturally, the government focuses on the pandemic response in cities. However, in reality, the taken measures also affect rural livelihood, especially farmers who have a very vital role in addressing the challenge that we face now: preventing food crisis.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.735
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.205
Teacher spread0.171 · 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