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Record W2903004791 · doi:10.22329/jtl.v12i1.4988

Academics Alone Together: Liberal Arts Graduate Students’ Writing Networks

2018· article· en· W2903004791 on OpenAlex
Mary Hedengren, Hannah V Harrison

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Teaching and Learning · 2018
Typearticle
Languageen
FieldHealth Professions
TopicDoctoral Education Challenges and Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsGraduate studentsLiberal arts educationWork (physics)Conjunction (astronomy)Peer feedbackAcademic writingVariety (cybernetics)PedagogyPsychologyMedical educationMathematics educationSociologyHigher educationComputer sciencePolitical scienceEngineeringMedicine

Abstract

fetched live from OpenAlex

Graduate writers who develop networks of writing are positioned to enter into the larger discourse community during and after graduate work. Our study surveyed graduate writers in the humanities about the sources of writing feedback they use and how much they trust or fear those sources. The results indicate that graduate students do employ a variety of sources and strategically assess when and how to use those sources. Still, many graduate students do not get frequent feedback on their writing, and others believe “we take what we get” in writing feedback. Student services who serve graduate students should work in conjunction with graduate program administrators and advisors to encourage students to develop effective networks of writing feedback, including important peer networks.

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.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.006
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 head0.250
GPT teacher head0.535
Teacher spread0.285 · 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