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Career Outcomes and Diversity: New Scholarship and Directions

2024· article· en· W4400440792 on OpenAlexaff
Maike Andresen, Janine Bosak, Douglas T. Hall, Béatrice van der Heijden, Bernadeta Goštautaitė, Najung Kim, Janice Lam, Marijke Verbruggen, Fida Afiouni, Eleni Apospori, Silvia Bagdadli, Bijana Bogicevic-Milikic, Jon P. Briscoe, Siriwut Buranapin, K. Övgü Çakmak‐Otluoğlu, Tânia Casado, Jean‐Luc Cerdin, Jongseok Cha, Katharina Chudzikowski, Richard Cotton, Michael Dickmann, Nicky Dries, Henrique Duarte, Anders Dysvik, Petra Eggenhofer‐Rehart, Sonia Ferenčíková, Leire Gartzia, Martina Gianecchini, Martin Gubler, Hugh Gunz, Madeline E. Heilman, Ivona Hideg, Robert Kaše, Svetlana N. Khapova, David Krajcik, Émilie Lapointe, Mila Lazarova, Sergio Madero, Wolfgang Mayrhofer, Eric J. Michel, Sharad Kumar Mishra, Leda Panayotopoulou, Emma Parry, Astrid Reichel, Lea Katharina Reiss, Richa Saxena, Florian Schramm, Yan Shen, Adam Smale, Pamela Suzanne, Bryndís D. Steindórsdóttir, Ingo Stolz, Mami Taniguchi

Bibliographic record

VenueAcademy of Management Proceedings · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsSimon Fraser UniversityUniversity of Toronto
Fundersnot available
KeywordsScholarshipDiversity (politics)SociologyPsychologyPolitical scienceAnthropology

Abstract

fetched live from OpenAlex

Academic publications on careers date back to the early 20th century. One of the earliest publications, for example, is Parson's trait and factor theory, which was developed in the early 1900s but not published until after his death in 1909. The continued interest in career studies since then is not surprising, as the

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)0.033
metaresearch head score (Gemma)0.033
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 score0.033
Threshold uncertainty score0.172

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0090.012
Science and technology studies0.0040.022
Scholarly communication0.0150.028
Open science0.0030.008
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0100.001

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 head0.091
GPT teacher head0.412
Teacher spread0.321 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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