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
We contend that big data and management for sustainability are very good bedfellows, in that many of the affordances big data provides are naturally aligned with sustainability concerns (e.g., multidimensional nature, collective actions, smart allocation of resources, efficiency priority). Notwithstanding this promising stepping off point, and the enticing analytical opportunities that an abundance of data will generate, we provide some reflections on big data and the most promising avenues of research it might inspire in the field of management and sustainability. In the first part of our essay, we explore what managers can do with big data to reinforce organizational sustainability and how different operational, strategic, and corporate activities are affected in this process. In the second part, we focus on what big data allows researchers to explore and examine, ranging from sustainability job descriptions through environmental metrics to industry transformation. We conclude by advocating for strong theoretical orientation in research on and with big data.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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