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Record W2170769436 · doi:10.47678/cjhe.v44i2.183760

Mapping disciplinary differences and equity of academic control to create a space for collaboration

2014· article· en· W2170769436 on OpenAlexaffvenue
Lynne Siemens, Jeff Smith, Yin Liu

Bibliographic record

VenueCanadian Journal of Higher Education · 2014
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTeamworkDisciplineEquity (law)Space (punctuation)Work (physics)Control (management)SociologyShared spaceKnowledge managementPublic relationsEngineering ethicsComputer sciencePolitical scienceEngineeringSocial science

Abstract

fetched live from OpenAlex

Academics are collaborating more as their research questions are becoming more complex, often reaching beyond the capacity of any one person. However, in many parts of the campus, teamwork is not a traditional work pattern, and team members may not understand the best ways to work together to the benefit of the project. Challenges are particularly possible when there are differences among the disciplines represented on a team and when there are variations in academic control over decision making and research direction setting. Disparities in these two dimensions create potential for miscommunication, conflict, and other negative consequences, which may mean that a collaboration is not successful. This paper explores these dimensions and suggests a space for collaboration; it also describes some benefits and challenges associated within various positions within the framework. Academic teams can use this tool to determine the place they would like to occupy within the collaboration space and structure themselves accordingly before undertaking research.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.212

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.031
GPT teacher head0.367
Teacher spread0.337 · 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

Citations19
Published2014
Admission routes2
Has abstractyes

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