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Record W2748304027 · doi:10.36510/learnland.v10i2.819

Women Reflect on Being Well in Academia: Challenges and Supports

2017· article· en· W2748304027 on OpenAlexaffvenue
Gabrielle Young, Michelle Kilborn, Christine Arnold, Saiqa Azam, Cecile Badenhorst, Jennifer Godfrey Anderson, Karen Goodnough, Leah Lewis, Xuemei Li, Heather McLeod, Sylvia Moore, Sharon Penney, Sarah Pickett

Bibliographic record

VenueLEARNing Landscapes · 2017
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMentorshipCriticismSet (abstract data type)Promotion (chess)NarrativePublic relationsPsychologyPersonal narrativeWork (physics)Action (physics)Sense of communityProcess (computing)Higher educationPedagogyMedical educationSociologySocial psychologyPolitical scienceEngineeringMedicineComputer science

Abstract

fetched live from OpenAlex

A narrative approach was adopted to explore the experiences of 13 women who pursued academic careers. Analysis of the personal reflective narratives uncovered themes common to the participants, also the authors of this study, which focused on striving to have work-life balance, personal and professional costs associated with being unwell, and the impact of academic work on families. Findings highlighted suggestions for being well in academia such as choose to engage in work and leisure activities that are enjoyable and maintain relationships. Suggestions for universities included: provide clear promotion and tenure processes, examine workload expectations, promote wellness, and facilitate mentorship. About Memorial University’s Faculty of Education Writing Group In 2009, a group of members from a Faculty of Education began meeting to share their writing and discuss the writing process. We meet regularly and each member takes a turn hosting the meeting. There are no strict deadlines and action items for the meetings; instead, each member takes a turn checking in with the group and asking for feedback or advice on their writing. It is a relaxed and open setting where networking and socializing are as welcome as producing results. The group deliberately set forth to create an environment of non-criticism—we can give feedback but not criticism and we agree to promote support, rather than competition. Through this process, the writing group has served to foster a sense of belonging.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.561
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.034
GPT teacher head0.323
Teacher spread0.290 · 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 designObservational
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

Citations8
Published2017
Admission routes2
Has abstractyes

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