MétaCan
Menu
Back to cohort
Record W4389074538 · doi:10.1215/9781478027669-002

Nostalgia

2023· book-chapter· en· W4389074538 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsRestructuringGovernment (linguistics)Work (physics)Meaning (existential)Political scienceSociologyPublic relationsEngineeringEpistemologyLaw

Abstract

fetched live from OpenAlex

Chapter 1 explores how environmental scientists living in Smithers, BC, articulated new senses of place and collectivity in the wake of government retreat. Rather than simply investing in new collaborative relationships, many scientists there have also articulated their work as contributing to a shared legacy of activism that they saw as defining the town’s history. These nostalgic articulations have become increasingly crucial to rural researchers’ efforts to define the meaning and boundaries of scientific communities in the absence of institutional structures. The chapter shows how rural researchers displaced by government restructuring have emplaced their expertise in emergent genres of local history. By articulating expertise to belonging, however, some researchers have also helped to obscure the forms of mobility that allow Euro-Canadian researchers to live and work in the northwest—a place to which, unlike their First Nations neighbors, the majority of them first moved by choice.

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)0.004
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.098
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0080.006
Scholarly communication0.0080.005
Open science0.0030.007
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0980.032

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 head0.056
GPT teacher head0.331
Teacher spread0.276 · 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 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicGeographies of human-animal interactionsFrench-language works237,207