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Record W4392471638 · doi:10.1177/01492063241231505

Many Roads to Success: Broadening Our Views of Academic Career Paths and Advice

2024· article· en· W4392471638 on OpenAlexaff
Beth A. Livingston, Jamie L. Gloor, Anna Katherine Ward, Allison S. Gabriel, Joanna Tochman Campbell, Emily S. Block, Dorothy Carter, Kimberly A. French, Rachel E. Frieder, Annika Hillebrandt, Jia Hu, Kristen P. Jones, Dana L. Joseph, Nina M. Junker, Ashley Mandeville, Sarah M. G. Otner, Amanda S. Patel, Samantha C. Paustian‐Underdahl, Manuela Priesemuth, Kristen M. Shockley, Mindy K. Shoss

Bibliographic record

VenueJournal of Management · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of WaterlooUniversity of Alberta
FundersBAIF Development Research FoundationSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsAdvice (programming)Field (mathematics)ElitePublic relationsPsychologySociologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Advice is often given to junior scholars in the field of organization science to ostensibly facilitate their career success. In this commentary, we discuss insights from 19 elite scholars (i.e., Fellows and top journal editors) about the advice they received–and, often, did not follow–throughout their careers. We highlight some of the pitfalls from the current, all-too-common, and often singular advice given to junior scholars while also adding necessary nuance to the requirements to achieve success in our field. We conclude with advice on how to give better advice, thereby more equitably encouraging a new generation of increasingly diverse researchers and future professors.

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.624

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.023
GPT teacher head0.264
Teacher spread0.241 · 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

Citations11
Published2024
Admission routes1
Has abstractyes

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