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Record W2097799459 · doi:10.1177/0013161x05278180

Teacher Nostalgia and the Sustainability of Reform: The Generation and Degeneration of Teachers’ Missions, Memory, and Meaning

2006· article· en· W2097799459 on OpenAlexaff
Ivor Goodson, Shawn Moore, Andy Hargreaves

Bibliographic record

VenueEducational Administration Quarterly · 2006
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEnthusiasmSustainabilityMeaning (existential)SituatedFeelingPoliticsPessimismPsychologyPedagogyEnergy (signal processing)SociologyPolitical scienceSocial psychologyLaw

Abstract

fetched live from OpenAlex

Purpose : This article focuses on the sustainability of reform through the lens of teachers’ nostalgia—the major form of memory among a demographically dominant cohort of experienced older teachers. Unwanted change evokes senses of nostalgia for these lost missions that take two forms: social and political. As teachers age, their responses to change are influenced not only by processes of degeneration (loss of commitment, energy, enthusiasm, etc.) but also by the agendas of the generation—historically situated missions formed decades ago that teachers have carried with them throughout their careers. Findings: Findings indicate that the effects of cumulative demographic and educational change and the resulting nostalgias have left teachers feeling resistant to mandated reform, insecure about their own professional capacity, disenchanted with their students, and pessimistic about their schools’ future. The results of this research have practical implications for policy makers, administrators, and classroom teachers.

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)0.005
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.018
Scholarly communication0.0070.006
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.251
Teacher spread0.236 · 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 designQualitative
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

Citations161
Published2006
Admission routes1
Has abstractyes

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