MétaCan
Menu
Back to cohort
Record W4306250572 · doi:10.1002/pra2.742

Academic Casualization, Precarity, and Information Practices: A Scoping Review

2022· review· en· W4306250572 on OpenAlexafffund
Rebekah Willson, Owen Stewart‐Robertson, Heidi Julien, Lisa M. Given

Bibliographic record

VenueProceedings of the Association for Information Science and Technology · 2022
Typereview
Languageen
FieldSocial Sciences
TopicEmotional Labor in Professions
Canadian institutionsMcGill University
FundersUniversity at BuffaloSocial Sciences and Humanities Research Council of CanadaMcGill University
KeywordsPrecarityTemporalitiesWork (physics)Public relationsSet (abstract data type)SociologyKnowledge managementEngineering ethicsPolitical scienceEngineering

Abstract

fetched live from OpenAlex

ABSTRACT In the first phase of a larger research project exploring the information practices of contract academic staff, a review of the wider literature around academic casualization was conducted. This research begins to address the need for greater understanding of the experiences of these workers in the information‐intensive environments of higher education. A scoping review methodology was applied, and both academic and grey literature from multiple disciplines was reviewed. The literature points to casualization in academia as a growing phenomenon that has important consequences for staff, students, and universities themselves. This poster discusses the initial themes emerging from the literature, including: the precarity and marginalization of academic staff, the unrecognized emotional labor shaping information practices, and the impacts of multiple temporalities on the work, careers, and lives of such staff. The findings suggest the need for empirical research to address the lack of knowledge around the information environments of contract academics and set the stage for the next phases of the research project.

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)0.009
metaresearch head score (Gemma)0.060
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.972
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.060
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0020.001
Scholarly communication0.0000.010
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.078
GPT teacher head0.440
Teacher spread0.363 · 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.

Study designNot applicable
Domainnot available
GenreReview

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
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the Association for Information Science and TechnologySame topicEmotional Labor in ProfessionsFrench-language works237,207