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Record W4205631677 · doi:10.3138/seminar.55.4.1

Activating Affinities

2019· article· en· W4205631677 on OpenAlexvenueno aff
Bradley Boovy, Natchee Blu Barnd

Bibliographic record

VenueSeminar A Journal of Germanic Studies · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsTransformative learningScholarshipGermanSociologyNeoliberalism (international relations)DisciplineAllianceColonialismIndigenousState (computer science)Power (physics)Political scienceSocial scienceLawPedagogy

Abstract

fetched live from OpenAlex

Through a discussion of the ways in which institutions foreclose or enable connections across disciplines, we argue that scholars in Indigenous Studies and German Studies can seek out strategic points of alliance with each other in spite of the divergent histories and commitments of the two disciplines. Drawing on the authors’ reflections on their collaborative relationship, the article describes possibilities for challenging and intervening in institutional power structures that seek to further align the values of higher education with the prerogatives of neoliberalism in a settler colonial and xenophobic state. The authors suggest that German Studies scholars draw on critical frameworks in activist disciplines such as Indigenous Studies as well as recent critical interventions within German Studies in order to reimagine and increase commitments to transformative scholarship and pedagogies.

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)0.013
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.081
Threshold uncertainty score0.270

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0090.009
Scholarly communication0.0090.011
Open science0.0020.014
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0810.013

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 head0.224
GPT teacher head0.516
Teacher spread0.292 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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