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Styles of Educational Leadership for Modernist and Postmodernist Approaches

2016· article· en· W2824164019 on OpenAlexaffabout
Sirous Tabrizi, Glenn Rideout

Bibliographic record

VenueInternational Journal for Infonomics · 2016
Typearticle
Languageen
FieldComputer Science
TopicEducational Challenges and Innovations
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPostmodernismAestheticsSociologyPolitical scienceEpistemologyArtPhilosophy

Abstract

fetched live from OpenAlex

Some researchers have identified differences in educational approaches depending on whether a modernist or postmodernist worldview is used. Furthermore, societies in different countries can also take a predominantly modernist or postmodern worldview, with Western countries being primarily postmodernist. Given that situation, there may be leadership styles that are more appropriate to an educational context that is predominantly modernist or postmodernist. Even though postmodernism suggests no "best" style of leadership is possible, since that requires being able to objectively measure leadership effectiveness, there may still be approaches that are most consistent with the postmodernist worldview. This paper explores such a possibility, examining differences between the worldviews and what factors are appropriate in each for educational leadership. Then, two case studies of different countries --Canada for a postmodernist education, and Iran for a modernist education -are briefly presented so that the exploration becomes more concrete.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.249
GPT teacher head0.342
Teacher spread0.093 · 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 designTheoretical or conceptual
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

Citations4
Published2016
Admission routes2
Has abstractyes

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