Learning from Practice in the Face of Conflict and Integrating Technical Expertise with Participatory Planning: Critical Commentaries on the Practice of Planner-Architect Laurence Sherman Mediation and Collaboration in Architecture and Community Planning: A Profile of Larry Sherman Practical Elements of Facilitative Leadership and Collaborative Problem Solving Where Do Collaborative Planning Instincts Come From? Lessons from the Field Words, Bodies, Things
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
Click to increase image sizeClick to decrease image size Notes 1. John Forester conducted this interview in Kitchener, Ontario on 2 June 2004. Thanks for transcription and editing help to Rachel Weiner and Christy Tao of Cornell's Department of City and Regional Planning. Edited by John Forester and then approved for classroom use by Larry Sherman on 16 August 2005, this profile has been re-edited by Larry Sherman (in April 2011) for publication in Planning Theory and Practice. 1. John Forester conducted this interview in Kitchener, Ontario on 2 June 2004. Thanks for transcription and editing help to Rachel Weiner and Christy Tao of Cornell's Department of City and Regional Planning. Edited by John Forester and then approved for classroom use by Larry Sherman on 16 August 2005, this profile has been re-edited by Larry Sherman (in April 2011) for publication in Planning Theory and Practice.
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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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