Plausibility indications in future scenarios
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
Quality criteria for generating future-oriented knowledge and future scenarios are different from those developed for knowledge about past and current events. Such quality criteria can be defined relative to the intended function of the knowledge. Plausibility has emerged as a central quality criterion of scenarios that allows exploring the future with credibility and saliency. But what exactly is plausibility vis-à-vis probability, consistency, and desirability? And how can plausibility be evaluated and constructed in scenarios? Sufficient plausibility, in this article, refers to scenarios that hold enough evidence to be considered ‘occurrable’. This might have been the underlying idea of scenarios all along without being explicitly elaborated in a pragmatic concept or methodology. Here, we operationalise plausibility in scenarios through a set of plausibility indications and illustrate the proposal with scenarios constructed for Phoenix, Arizona. The article operationalises the concept of plausibility in scenarios to support scholars and practitioners alike.
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 | 0.001 | 0.001 |
| 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