Bibliographic record
Abstract
Visioning became widely used in the planning process during the 1980s and 90s. In its simplest form it is the notion of creating images of the future to serve as goals or guides for planning decisions. For many people the idea of visioning became synonymous with or closely linked to public participation. While hundreds of communities have undertaken visioning, there has been little or no examination of the theoretical underpinnings of the practice. Practitioners of the technique, whether consultants or municipal planners, seem to have worked largely from a set of tacit assumptions about the usefulness of the practice. This study examines literature from various disciplines, refers to planning documents and reports on interviews and questionnaires in order to articulate the underlying assumptions or theory-like statements about visioning and then to measure those assertions against existing research. The resulting analysis shows that while there is a basis to support some of the assumptions about visioning there are also profound weaknesses in parts of the underlying theory. The paper is intended to help both advocates and critics of visioning, as well as those with a more general interest in planning, to better understand and assess visioning and other techniques that enter the professional lexicon.
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.
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 | 0.046 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.011 | 0.116 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".