MétaCan
Menu
Back to cohort
Record W3124277450 · doi:10.1177/0033688220981778

“COVID-19 is an Opportunity to Rediscover Ourselves”: Reflections of a Novice EFL Teacher in Central America

2021· article· en· W3124277450 on OpenAlexafffund
Thomas S. C. Farrell, Connie Stanclik

Bibliographic record

VenueRELC Journal · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsBrock University
FundersBrock University
KeywordsPsychologyPedagogyInterdependenceInterpretation (philosophy)Reflection (computer programming)Reflective practiceDivergence (linguistics)Teacher educationQualitative researchMathematics educationSociologyLinguistics

Abstract

fetched live from OpenAlex

This article presents a case study that examined the principles and practices of one novice English as a foreign language (EFL) teacher at a prominent English language institution in Central America. This qualitative study sought to contribute to the discussion of the perceived interdependent influences of EFL teachers’ thoughts, identities, and behaviors through five stages of self-reflection in Farrell’s framework for reflective practice. The EFL teacher engaged in conscious reflection to subject their beliefs to critical analysis and interpretation expressed through their philosophy, principles, theory, practice, and beyond practice. Overall, the findings confirm that reflections in all five stages are connected to several common themes, but simultaneously reveal a complex relationship between the teacher’s stated principles and actual practice. The discussion explores potential reasons for convergence and divergence in teachers’ beliefs and classroom actions, concluding that the results correlate with previous research in the field of language education and teacher reflection.

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)0.006
metaresearch head score (Gemma)0.014
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 score0.062
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0250.018
Scholarly communication0.0070.004
Open science0.0030.008
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0020.001

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 head0.113
GPT teacher head0.364
Teacher spread0.251 · 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

Citations34
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueRELC JournalSame topicEFL/ESL Teaching and LearningFrench-language works237,207