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Record W4313387993 · doi:10.1007/978-981-19-2080-6_86

Introduction to Organization, Leadership, and Change in ODDE

2023· book-chapter· en· W4313387993 on OpenAlexaff
Ross Paul

Bibliographic record

VenueHandbook of Open, Distance and Digital Education · 2023
Typebook-chapter
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of British Columbia
FundersJapan Society for the Promotion of ScienceBrigham Young University
KeywordsPolitical scienceContext (archaeology)Theme (computing)Public relationsManaging changeSociologyGeographyComputer science

Abstract

fetched live from OpenAlex

Abstract This chapter provides an overview of the 11 other chapters in Section 4 of the handbook which address issues of Organisation, Leadership and Change. It pays particular attention to the impact of the Covid-19 pandemic on perceptions of ODDE, noting both the benefits of the greatly enhanced international interest in on-line learning and the negative perceptions associated with its misuse during the sudden demand for emergency remote teaching in conventional educational institutions. It envisions a blurring of distinctions between conventional and ODDE institutions with consequent opportunities for the latter. While these issues are pursued through various perspectives in the Section 4 chapters, there is a unifying theme of the critical importance of institutional leadership throughout and a concomitant focus on how leadership has to change in a rapidly evolving international context. The chapter envisions a bright future for ODDE but only if critical issues of institutional leadership are addressed and if those leading conventional institutions are made aware of the research and experience emanating from the established ODDE sector.

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)0.000
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0330.008

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 head0.126
GPT teacher head0.400
Teacher spread0.274 · 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
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

Citations1
Published2023
Admission routes1
Has abstractyes

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