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Record W2770315563 · doi:10.19173/irrodl.v18i7.3207

Striving Toward Openness: But What Do We Really Mean?

2017· article· en· W2770315563 on OpenAlexvenueno aff
Vivien Rolfe

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2017
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsnot available
Fundersnot available
KeywordsOpenness to experienceMainstreamPublic relationsSociologyPedagogyPerspective (graphical)Higher educationService (business)PsychologyPolitical scienceSocial psychologyBusinessMarketingComputer science

Abstract

fetched live from OpenAlex

The global open education movement is striving toward openness as a feature of academic policy and practice, but evidence shows that these ambitions are far from mainstream, and levels of awareness in institutions is often disappointingly low. Those advocating for open education are seeking to widen engagement, but how targeted and persuasive are their messages? The aim of this research is to explore the voices often unheard, those of the teachers and professional service staff with whom we are engaging. This research presents a series of interviews with those involved in open education at De Montfort University in the UK, with the aim of gaining a better perspective of what openness means to them. The interviews were analysed through an interpretive lens allowing each individual to create their own story and reflect their own personal view of openness. The results of this study are that in this university, openness is represented by five elements – staff pedagogy and practice, benefits to learners, accessibility and access to content, institutional structures, and values and culture.This work shows the importance of adopting critical approaches to gain a deeper understanding of the philosophical and pedagogic stances within institutions. By giving a voice to all those involved we will be able to develop appropriate and more persuasive arguments to widen our sphere of influence as a community of open educators.

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)0.009
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.745
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0050.003
Open science0.0060.004
Research integrity0.0000.001
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 head0.124
GPT teacher head0.453
Teacher spread0.329 · 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.

Study designOther design
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

Citations14
Published2017
Admission routes1
Has abstractyes

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