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Record W2504251096 · doi:10.1007/978-94-6209-872-5

The Teacher, Literature and the Mediterranean

2014· book· en· W2504251096 on OpenAlexaff

Bibliographic record

VenueComparative and international education · 2014
Typebook
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsWestern University
Fundersnot available
KeywordsMediterranean climateMathematics educationGeographyPsychologyArchaeology

Abstract

fetched live from OpenAlex

At a time when the Mediterranean has rediscovered its own vitality, seven academics from the fields of education and literature look at how fictions set in the region narrate the role of the teacher from the point of view of the students and from that of the teachers themselves. While an increasingly technocratic approach to the performance of teachers focuses on competences, these often highly subjective narratives tell stories of practitioners who refuse to fit into the mould imposed on them by patriarchy or the educational institutions. The writers dealt with in this volume are aware that teachers cannot be solely defined in terms of what they are expected to do within schools and classrooms. This reductively conceives them as simply needing the skills to teach without having the ability to contextualise their teaching within wider historical, social and cultural realities. With its migration flows and intricate web of social and cultural politics, the Mediterranean of the 21st century is an ideal space for reflections on the role of the teacher in an ever-changing society.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.009
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.164
GPT teacher head0.446
Teacher spread0.282 · 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
Published2014
Admission routes1
Has abstractyes

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