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Record W1508456511 · doi:10.29173/pandpr19813

Teacher Praise and Encouragement: Towards an Education for Democracy

2008· article· en· W1508456511 on OpenAlexvenueno aff
Herner Sæverot

Bibliographic record

VenuePhenomenology & Practice · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicPhilosophy, Ethics, and Existentialism
Canadian institutionsnot available
Fundersnot available
KeywordsPraiseArgument (complex analysis)DemocracyCurriculumPedagogyPsychologyMathematics educationHospitalitySociologySocial psychologyPolitical scienceLawPolitics

Abstract

fetched live from OpenAlex

International education tests, such as the Organization for Economic Co-operation and Development (OECD) International Student Assessment (PISA) and Trends in International Mathematics and Science Study (TIMSS), steer the debate about education, form the image of a school, and specify the goals and objectives of the curriculum. Thus praise, prizes and other positive reinforcements become common methods in school. Through Franz Kafka's Letter to his Father I examine encouragement and teacher praise, which are, when we look into it, held in the grip of behaviourism. I take issue with this dominant thinking and make an attempt to go beyond praise and encouragement as positive reinforcements of desired behaviours that are postulated in advance. I make an educational argument out of Heidegger's phenomenological term, Ereignis, and suggest that teachers ought to praise that which the student is suited for and not only when they have fulfilled the demands of the curriculum. I also make an educational argument out of two Derridaean terms, hospitality and forgiveness, and claim that teachers ought to give back -- through praise and encouragement -- the students' self-respect, whatever their faults. Instead of being manipulative and serving narcissistic needs, praise ought to serve an education for democracy.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.863
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.090
GPT teacher head0.334
Teacher spread0.244 · 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.

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

Citations5
Published2008
Admission routes1
Has abstractyes

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