A Study on Facilitating Factors and Barriers to Addressing Cumulative Effects
Bibliographic record
Abstract
Consideration of cumulative effects (CE) has not been successful due not only to the nature of complexity and uncertainty of cumulative effects, but also to the lack of understanding about well defined facilitating factors and barriers for addressing cumulative effects. As a theoretical framework for understanding facilitating factors and barriers, this study used three criteria critical for improving knowledge systems: salience, credibility, and legitimacy. With an aim to understand how addressing CE is facilitated or obstructed in terms of salience, credibility, and legitimacy, the study reviewed three cases: Middle Humber in UK, Transboundary Crown of the Continent, and Great Sand Hills in Canada. As a result, it was found that salience of CE approaches is facilitated under cooperative consulting among key stakeholders, but is obstructed when fails to secure support from political sectors. Credibility of CE approaches is promoted when modeling allows flexible use of scenario sets for analyzing tradeoffs to arrive at a satisfactory option, but is weakened when modeling fails to coordinate different formats of data, scale, and local interests across various administrative jurisdictions. Legitimacy is elevated by early involvement of stakeholders in scenario development with ongoing communication through a variety of media, but is threatened when affected stakeholders are not fully considered and regulatory authorities are less involved.
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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 | 0.043 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".