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Record W2177356444 · doi:10.19030/ajbe.v5i2.6818

Using Popular Film As A Teaching Resource In Accounting Classes

2012· article· en· W2177356444 on OpenAlexafffund
Darlene Bay, Sandra Felton

Bibliographic record

VenueAmerican Journal of Business Education (AJBE) · 2012
Typearticle
Languageen
FieldHealth Professions
TopicFilm in Education and Therapy
Canadian institutionsBrock University
FundersQueen's University
KeywordsResource (disambiguation)AccountingTeaching methodMathematics educationPsychologyComputer scienceBusiness

Abstract

fetched live from OpenAlex

This paper describes a pedagogical experiment that used feature films in a senior accounting class to stimulate development of student competencies and raise ethical issues. Rather than being content driven, this active learning technique focuses on skills development, while engaging the students’ emotions in the learning process. Encompassing three types of knowledge (conceptual, procedural and meta-cognitive), the exercises explore concepts of internal control, corporate governance and business ethics. They provide opportunities for accounting students to practice the higher level cognitive skills in Bloom’s (1956) taxonomy and aim to foster students’ emotional commitment to ethical decision-making. Students reacted positively to these activities, finding them most helpful in clarifying the impact of ethical issues. We observed significant differences between the beliefs of groups who participated in these exercises and those who did not, suggesting that this activity can be an effective tool for engaging students and influencing their perceptions about accounting issues.

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)0.002
metaresearch head score (Gemma)0.007
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.059
GPT teacher head0.456
Teacher spread0.397 · 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
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

Citations23
Published2012
Admission routes2
Has abstractyes

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