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Record W4213253004 · doi:10.4324/9781003127413-4

A national approach

2022· book-chapter· en· W4213253004 on OpenAlexaboutno aff
Martha Caddell, Sam Ellis, Christine Haddow, Kimberly Wilder-Davis

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

This chapter explores programme leadership in a collaborative, multi-institutional context arguing cross-institutional connection offers programme leaders (PLs) opportunities for greater depth of learning and opportunities to build networks and platforms that enhance both the value and prestige of their leadership position. The chapter reflects on the authors' work in Scotland establishing a cross-sector ‘Collaborative Cluster’ tasked with better understanding the diversity of approaches taken to programme leadership, sharing experience and practical resources and identifying areas for collaborative learning and development. Bringing together the PLs and those responsible for enhancing institutional practice and support, the Collaborative Cluster succeeded in developing a Scotland-wide schedule of work to address some key challenges including leading without authority, role confusion, working with programme-level data and a lack of role-specific opportunities for professional development. Exploring the work of the Cluster, the chapter offers evidence of value, success and inspiration for action in the following three key areas: Vignettes of practice from the frontline of programme leadership Collaboration for developing institutional and individual conversation to initiate changed practice Recognition and reward for those in PL roles Webb's Canadian practitioner response reflects on the value of this collaborative approach to understanding and supporting programme leadership.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.628
Threshold uncertainty score1.000

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)0.0540.001

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 head0.120
GPT teacher head0.238
Teacher spread0.118 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations1
Published2022
Admission routes1
Has abstractyes

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