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Record W4394702965 · doi:10.25071/jhv57k13

Canada: Homeland or hostile land?

2017· article· en· W4394702965 on OpenAlexaboutno aff
Jennifer Mussell, Erin Yunes

Bibliographic record

VenueCanada Watch · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsHomelandHomeland securityComputer securityPolitical scienceEnvironmental planningGeographyComputer scienceArchaeologyTerrorismLawPolitics

Abstract

fetched live from OpenAlex

hosted its third annual graduate student conference, entitled "Canada: Homeland or Hostile Land?"Over the course of the two-day conference, more than 50 students from universities across the countr y presented their work and engaged in critical exploration of inequalities in Canadian society.Panels and papers ranged in subject from Canadian settler colonialism and its legacies, to multiculturalism, to state policy and its impacts on minorities.Despite the diversity of topics and range of perspectives, all the discussions that ensued featured a common conclusion: that Canada has both a history and a present characterized by deeply entrenched social and economic inequalities along lines of gender, race, indigeneity, ability, region, socio-economic status, and migration status, among others.As Canada approaches its 150th birthday celebrations, there is no better time to reflect on the fact that, for some, Canada is more hostile land than homeland.This issue includes 11 essays, each of which was developed from a presentation given at the conference.The first

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)0.002
metaresearch head score (Gemma)0.005
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: Empirical · Consensus signal: none
Teacher disagreement score0.085
Threshold uncertainty score0.614

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0120.009
Scholarly communication0.0120.004
Open science0.0020.003
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0240.002

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 head0.019
GPT teacher head0.264
Teacher spread0.245 · 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
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
Published2017
Admission routes1
Has abstractyes

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