MétaCan
Menu
Back to cohort

The Case for Decision‐Forcing Cases: Preparing Teachers for EFL Settings

2000· article· en· W1950459092 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTESOL Journal · 2000
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsFeelingConversationWonderPsychologyClass (philosophy)Mathematics educationStairsPedagogyHistorySocial psychologyCommunicationComputer scienceArchaeology

Abstract

fetched live from OpenAlex

As Mary climbed the three flights of stairs to the 4B (Grade 10) classroom, she wondered how well her conversation class would go. She was usually pretty upbeat, but today she had an uneasy feeling in the pit of her stomach. When she finally reached the classroom, the math teacher was still not finished. It would be another short session—probably just 25 or 30 minutes. Seeing the students only once every 6 days didn't help either. The year was almost over and she still didn't know most of their names. No wonder! She was seeing over 700 students each week! Mary Martin, a NET (native‐English‐speaking teacher) from Australia with almost 20 years of teaching experience, had only been at the Chan Chu Secondary School for 8 months and was still feeling her way around. This was her first EFL experience, and Hong Kong was a bit of a shock. She was really enjoying it, but there were so many things to adjust to. A few weeks ago, she decided to try using cooperative learning techniques in her classroom to provide more opportunity for her students to practice their English in small groups. They were much more at home with “chalk and talk,” but Mary was determined to experiment, even though they were generally resistant to anything new. Crammed into a small, stuffy room were 43 science students, 37 boys and 6 girls. Most were at the Band 4–5 level (low achievers on exams) and were not motivated to learn English. Many seemed completely apathetic and dispirited. Some even acted scared of her, although that was changing. In the corridor, several students were now saying hello to her instead of shifting their eyes and rushing away. She was pleased about that. After the math teacher exited the class, Mary took a deep breath and entered. The students pushed back their chairs, stood up, and recited, “Gooooood afternooooon, Miiiiiisssss Maaaaartin.” Mary smiled and greeted the class before asking them to take their seats. She wasn't sure if she'd ever get used to the formality. Mary clapped her hands and called out: “Today, we are doing group work, which means we are ‘fighting against the clock.‘ What do I mean by ‘fighting against the clock?’ I mean that we are going to have to quickly move our tables and chairs today. We are going to have to sit in groups of four, okay?” (excerpted from Jackson, 2000)

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.054
GPT teacher head0.432
Teacher spread0.378 · 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