Making Management (More) Relevant: Breaking Down Disciplinary Walls and Pursuing Neglected Independent Variables
Bibliographic record
Abstract
The Walls Project encourages educators to broaden management teaching beyond individual and organizational variables and outcomes to systemic variables and outcomes. Its focus is on discovering independent variables that have social and environmental impacts and are currently neglected. Founded by six individuals who met at a RMLE UnConference in 2017, the Project decided to share pedagogical materials, examine them for commonalities, and present their findings at the MOBTC conference in 2019. This article summarizes these materials with an eye to revealing several variables of consequence, such as socioeconomic status and belief in economic growth, which are studied and taught infrequently in business schools. We suggest that researchers examine business curricula for similar neglected variables, study their impact across systems levels, and then develop them pedagogically to enhance management education that has a social and environmental impact.
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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 | 0.042 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".