MétaCan
Menu
Back to cohort
Record W7003680365

9783111519791.pdf

2024· other· en· W7003680365 on OpenAlexaboutno aff

Bibliographic record

VenueOAPEN (The OAPEN Foundation) · 2024
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsEthnographyWork (physics)Natural (archaeology)DoorsValue (mathematics)Sense of place
DOInot available

Abstract

fetched live from OpenAlex

About this book When most of their jobs disappear, how do communities survive? In the hard-hit area explored in this book – the Bonavista Peninsula, on the island of Newfoundland – many residents transitioned into "everyday" entrepreneurs such as restauranteurs. Rural Revival explains how these business owners developed a place rich in "entrepreneurial capital." The author draws on six years of ethnographic fieldwork in the area: observations from listening, watching and learning with people in their everyday settings. Camera work opened doors to people’s ventures and their lives. The many photographs in this book bring you deeply into a sense of presence among the people and their natural settings. To interpret the findings from fieldwork, the author draws on rural sociology and economic anthropology. He shows how people transformed the value of once-neglected things in the "house economy" into assets for tourists, leaving the "market economy." He uses theories of "cross-sector partnerships" to show the ways in which regional development is tough to sustain.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.069
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0070.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.9310.929

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 head0.007
GPT teacher head0.272
Teacher spread0.266 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueOAPEN (The OAPEN Foundation)Same topicCell Image Analysis TechniquesFrench-language works237,207