Enhancing the Positive Impact Rating: A New Business School Rating in Support of a Sustainable Future
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.
Bibliographic record
Abstract
Business school rankings are “big business”, influencing donors and potential students alike, holding much sway over decanal and faculty priorities, particularly with respect to the curriculum as well as the focus and destination of research publications (i.e., in so-called “top” journals). Over the past several years, the perverse effects of these priorities have begun to be acknowledged, and new ratings and ranking systems have emerged. One promising newcomer is the Positive Impact Rating (PIR), which uniquely and exclusively focuses on student perceptions of their business school’s priorities and the learning experience. In addition, it organizes schools by tier, in an effort to foster collaboration and continuous improvement, as opposed to ranked competition. If this new approach is to achieve its stated objective and help shift the focus of business schools to developing future business leaders and research output in alignment with a more sustainable world (and the United Nations Sustainable Development Goals), it is essential that the metrics used be and be perceived as both valid and reliable. The current research aims to make a contribution in this regard, analyzing the results at one business school in detail and making recommendations for strengthening these aims. Results show that the parametric properties of the survey are highly interrelated, suggesting that the predictive utility of the separate elements within the scale could be improved. Additionally, biases in scores may exist depending on where the responses are collected and who solicited them, as well as the students’ perception of their overall academic experience and on socio-cultural factors.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it