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Record W4405978324 · doi:10.26522/jiste.v28i2.4768

Transforming Student Discipline: A Teacher’s Reflective Essay on Moving From Punishment to Compassion in Education

2024· article· en· W4405978324 on OpenAlexaff
William Sarfo Ankomah

Bibliographic record

VenueJournal of the International Society for Teacher Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsCompassionPunishment (psychology)PedagogyPsychologyEmpathySociologyMathematics educationSocial psychologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

In this reflective essay, I examine the impact of corporal punishment in Ghanaian schools by drawing on my experiences as both a victim and a former perpetrator of these practices. Through reflecting on the punitive discipline I endured and later enforced, I highlight the detrimental effects on students’ well-being, self-worth, and engagement in learning. Guided by Parker J. Palmer’s (1997, 2015) concept that “we teach who we are,” I point out the need for educators to embody compassion, empathy, and self-awareness. I also advocate for eliminating punitive discipline in favor of nurturing, supportive, and non-violent relationships that consider the broader socioeconomic and personal contexts influencing student behavior. By creating, sustaining, and promoting a caring, inclusive, and empowering school environment, I argue that teachers can address the cycle of fear and humiliation that stifles student engagement and instead create a space where students feel valued and motivated to develop their full potential.

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)0.019
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: Other · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0070.019
Scholarly communication0.0060.006
Open science0.0010.004
Research integrity0.0050.016
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.416
Teacher spread0.388 · 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
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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