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Record W7074601361

Poetic Fabulations: Chartering Relationalities of Black Flourishing, Mutuality, Inclusive Excellence, and Accountability

2024· article· en· W7074601361 on OpenAlexaffabout

Bibliographic record

VenueJournals @ The Mount (Mount Saint Vincent University) · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Resources Studies
Canadian institutionsUniversity of VictoriaAthabasca UniversityUniversity of British Columbia
Fundersnot available
KeywordsWitnessAccountabilityPoetryCharterInterruptRhetorical questionDictionDerogation
DOInot available

Abstract

fetched live from OpenAlex

This collective work (four authors) demonstrates how persistent structures in higher education are mobilized in the signing of the Scarborough Charter on Anti-Black Racism and Black Inclusion in Canadian Higher Education: Principles, Actions, and Accountabilities. We read this event against the grain, as an act requiring relation-building and accountability. Recognizing the promises and risks of this work, and inspired by Black Feminist/coalitional practices which disorient from pre-mapped routes and knowledges in universities and reorient to otherwise ways of being, we name this process “poetic fabulation.” We begin with poetry and proceed with seven stanzas that orient thematic reflections in each prose section that follows. The multi-vocality of the piece gives evidence to the experiences of the authors in this work. The interregnum which follows stanza 4, functions at the simultaneous and unruly registers of poetry, analysis, affect, and the somatic, to interrupt the flow, signalling how we experience labouring within the academy. Our work is collaborative, but also entails being hailed and responding in different ways. Using a full spectrum of creative and analytic skills, we navigate towards shared goals to process what we witness in and across post-secondary institution(s), to hold and care for the impacts of discretionary power.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.731
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.248
Teacher spread0.214 · 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 teacher head, 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
Published2024
Admission routes2
Has abstractyes

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