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Record W4312176108 · doi:10.33137/cjal-rcbu.v8.38451

Contract Academic Librarians in Canada

2022· article· en· W4312176108 on OpenAlexaffvenueabout
Lindsay McNiff, Nicole Carter

Bibliographic record

VenueCanadian Journal of Academic Librarianship · 2022
Typearticle
Languageen
FieldPsychology
TopicHuman Resource Development and Performance Evaluation
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGateway (web page)Meaning (existential)DemographicsPsychological contractSet (abstract data type)Public relationsPsychologyField (mathematics)Medical educationSociologySocial psychologyPolitical scienceMedicineWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

The temporary contract is often framed to Master of Library and Information Studies (MLIS) graduate students as a key gateway into the field of academic librarianship (Lacey 2019), and yet outside of a few important studies and personal reflections, literature on this topic is relatively scarce. This paper reports on the demographics of participant academic librarians who have held temporary contracts in Canada, their career paths, the conditions under which they held contracts, and their experiences of workplace integration and other positive and negative outcomes. Study participants (n=95) have held one or more temporary contracts as an academic librarian in Canada during their career. An online survey was distributed, asking closed and open-ended questions. The data were analyzed using Excel, Qualtrics, NVivo, and manual methods. Participants derived new skills, new networks, satisfaction and confidence from their contract experiences (though sometimes only in retrospect), while others felt excluded, overworked, undervalued, and prevented from making life decisions. And many felt all these things at the same time, meaning that contract academic librarians are caught in a difficult set of competing structural and emotional experiences.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.509

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.010
Science and technology studies0.0260.005
Scholarly communication0.0070.002
Open science0.0020.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.001

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 head0.056
GPT teacher head0.278
Teacher spread0.222 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Other design
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
Published2022
Admission routes3
Has abstractyes

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