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Undergraduate curricula in the USA, Canada, New Zealand and Australia: Are we missing the mark on Indigenous peoples and parks?

2020· article· en· W4317909986 on OpenAlexaboutno aff
Chance Finegan

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousCurriculumGeographyPolitical scienceArchaeologyLibrary scienceEcologyLawBiologyComputer science

Abstract

fetched live from OpenAlex

Indigenous peoples’ rights increasingly demand the attention of government agents, including protected area managers in the CANZUS states (Canada, Australia, New Zealand and the USA). Park/Indigenous relations are a fundamental job competency for CANZUS park employees. This exploratory research draws on the curricula of 391 university major programmes to quantify the extent to which CANZUS university programmes in natural resources management, park management and allied fields might prepare aspiring CANZUS park employees to work with Indigenous peoples; I conclude the programmes generally fail to do so. Zero American park management majors in the study require Indigenous-focused coursework. In the Commonwealth countries, 52 per cent of park management programmes do so. Only 6 per cent of American natural resources management majors require such coursework, versus 45 per cent in the Commonwealth countries. This calls attention to an urgent need to improve aspiring park employees’ understanding of how their work intersects with Indigenous peoples and settler-colonialism.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.224
Threshold uncertainty score0.451

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.330
GPT teacher head0.478
Teacher spread0.148 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2020
Admission routes1
Has abstractyes

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