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Record W7008351220

Building Knowledge about Variability in the Abundance and Distribution of Natural Resources: A Case Study on Berry Harvesting from Northern Canada

2009· article· en· W7008351220 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

VenueDigital Library Of The Commons Repository (Indiana University) · 2009
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsAbundance (ecology)Distribution (mathematics)Natural (archaeology)Traditional knowledgeCommon knowledge (logic)Berry
DOInot available

Abstract

fetched live from OpenAlex

"Local and traditional knowledge is disappearing at an alarming rate, however, there are examples in many parts of the world, including northern Canada, where new knowledge is being created. This case study on berry harvesting provides valuable insight into how knowledge is generated; specifically knowledge about variability in the abundance and distribution of a common pool resource. Knowledge is created when observations about changes are interpreted and shared from year to year within a family group, the community or across the region. When this knowledge is shared and interpreted over many generations, traditional knowledge is generated. It is argued that the success of berry harvesting in any given year, is dependent upon feedback between what is observed and interpreted and the decisions women make about where, when and with whom to harvest."

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.997

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)0.0000.000

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 head0.005
GPT teacher head0.161
Teacher spread0.156 · 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