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Record W3169889871

Academic Dignity: Countering the Emotional Experience of Academia.

2020· article· en· W3169889871 on OpenAlexaffvenueabout
Sandra G. Kouritzin, Erica Kolomic, Taylor Ellis, Satoru Nakagawa

Bibliographic record

VenueCanadian Journal of Educational Administration and Policy · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEmotional Labor in Professions
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsDignityConceptualizationSociologyNeoliberalism (international relations)Extant taxonIdeologyPedagogyCollegialitySocial sciencePolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

A significant emerging body of research has articulated and critiqued the conditions and impacts of neoliberalism on academic institutions, particularly how the rise of the 'marketized' university has shifted the way in which academics are expected to engage in research and teaching. We highlight some of the key concepts evident in the research as they pertain to the emotional responses of academics to the ideological shift that has taken place within the academy. We focus on their perspectives in order to validate the emotional and lived experiences of academics and repudiate the neoliberal conceptualization of the academic as homo economicus. Referencing our ongoing research in Canadian universities as well as extant literature, we overview work on workplace dignity, a countervailing notion that mobilizes positive emotional concepts. Using this as a springboard, we finally begin to articulate an academic dignity-centered approach. By ‘academics’, we mean all those engaged in academic work within the academy, from tenured faculty to research specialists and contracted instructors.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.071
GPT teacher head0.410
Teacher spread0.339 · 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 designTheoretical or conceptual
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

Citations5
Published2020
Admission routes3
Has abstractyes

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