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Record W2472083464 · doi:10.1080/13573322.2016.1168795

Paying the piper: the costs and consequences of academic advancement

2016· article· en· W2472083464 on OpenAlexaff
Ashley Casey, Tim Fletcher

Bibliographic record

VenueSport Education and Society · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsBrock University
Fundersnot available
KeywordsWorkforcePublic relationsPsychologyHigher educationSociologyMedical educationPolitical scienceMedicineLaw

Abstract

fetched live from OpenAlex

In many professions there are qualifications to gain and professional standards to achieve. Lawyers pass the bar and doctors pass their boards. In academic life the equivalent is a doctorate, closely followed by a profile of peer-reviewed publication. To hold a doctoral degree is the common requirement to become ‘academic’ but does it prepare individuals to advance in an academic career? In choosing the idiom ‘paying the piper’ (i.e. where one must pay the costs and accept the consequences of one's actions) we recognise that in seeking to develop our scholarly profiles we had to choose to adapt successfully to global workplace expectations, modify our professional aspirations or refuse to participate. In this paper we examine the challenges we faced as academics in physical education as we progressed from beginning to mid-career stages. We focus particularly on challenges related to seeking external research funding, exploring our assumptions about academic life and the perceived expectations that lie under the surface around research funding, teaching and service. Through the use of self-study we demonstrate how our perceptions of academic career progress meant paying personal and professional costs that we were largely (and perhaps naively) unaware of when we entered the academic workforce. Data consisted of Ashley’s reflective diaries generated over the past six years, which were analysed deductively based on an understanding of salient experiences of academic life, most notably, those related to the pursuit of funding and its relationship to academic advancement. Tim played the role of critical friend by asking probing questions, relating personal experiences to instances in Ashley's data, and offering alternative interpretations of Ashley's insights. By sharing our experiences we hope early career academics (ECAs) may relate to and learn from our naivety. In this way, there may be implications for the induction and mentoring of future ECAs.

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)0.014
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.986
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.045
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0170.023
Scholarly communication0.0160.015
Open science0.0020.012
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0050.001

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 head0.070
GPT teacher head0.439
Teacher spread0.370 · 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.

Study designQualitative
DomainIncentives
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

Citations26
Published2016
Admission routes1
Has abstractyes

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