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Scaling the Peaks of Research

2013· article· en· W4401653534 on OpenAlexaff
Janelle McKenzie

Bibliographic record

VenueTeachers Work · 2013
Typearticle
Languageen
FieldHealth Professions
TopicDoctoral Education Challenges and Solutions
Canadian institutionsDoug Bragg Enterprises (Canada)
Fundersnot available
KeywordsScalingStatistical physicsPhysicsMathematics

Abstract

fetched live from OpenAlex

For many people undertaking research at Masterate or Doctorate level is a mountain too far. Yet for teachers taking on the challenge of research it can open new doors, invigorate practice and help them learn something about themselves. This article looks at the journey taken by one teacher to conquer the lofty heights of academia and the changes brought about through this journey. The teacher had to face the realisation that people often saw her in a completely different light to how she saw herself. Not only did she find that she had a value to others but that there were also expectations placed upon her which she felt obliged to meet. Yet what became most surprising to her in scaling the research mountain was that her own expectations outshone anything others placed upon her. She found that in pushing herself just that little bit further she began to unlock a belief in who she was and what she could actually achieve.

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)0.077
metaresearch head score (Gemma)0.159
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: Commentary · Consensus signal: none
Teacher disagreement score0.077
Threshold uncertainty score0.407

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0770.159
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0100.007
Science and technology studies0.0140.023
Scholarly communication0.0390.045
Open science0.0050.040
Research integrity0.0070.019
Insufficient payload (model declined to judge)0.0340.013

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 head0.534
GPT teacher head0.635
Teacher spread0.101 · 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
GenreCommentary

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
Published2013
Admission routes1
Has abstractyes

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