Understanding the Behavioral Paradox of the Companies’ by Using "The Corporation" Documentary
Bibliographic record
Abstract
Films are widely used in business education to illuminate management concepts. Since films can provide a version of how theories and concepts can actually be put into practice, they have more lasting impression. On the other hand; ethical issue are complicated and they involve many processes and influences that are diverse and interlinked. That’s why it is difficult for students to understand potential conflicts of interest if they lack business experience or frame of reference. In this study, “The Corporation”, a documentary film by March Achbar, Jennifer Abbott and Joel Bakan, which has received awards in film festivals around the world, has been used for analysis to illustrate the behavioral paradox of corporations. Film analysis has been used as an educational tool in order to teach organizational behavior and management concepts since 1970s. To check our assumption we have designed a study to explore whether using "The Corporation" documentary in classroom settings will raise the awareness of the students about the role that corporations play in ethical, social, and environmental issues which are essential for business decisions, and thus enable the students -the future business managers-, to understand the paradoxical behaviors of corporations. After the students watched “The Corporation”, a quantitative analysis has been conducted by comparing the written essays of the students as regards their interpretation of the film.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".