Bibliographic record
Abstract
VOL. 34 | NO. 3 | 2002 31 WITH THE PROLIFERATION of pre-packaged teambuildingtools, OD practitioners are exposed to a wide varietyof options for helping clients improve team effective-ness and solve group problems. Although the convenience of these instruments makes it tempting for practitioners – new and old — to rely on them, there is often limited evidence of their validity or the theoretical model(s) used to inform them. Without a strong understanding of group development the-ory, the practitioner is simply guessing at which tool will be appropriate for a given client situation. For some, the “favorite” tool becomes the solution to every group problem, leading to inaccurate diagnoses and unnecessarily limited opportunities for intervention. By contrast, the practitioner with a solid grounding in group development theory is in a better position to accurately diag-nose group problems, intervene effectively, select instruments and models with some assurance of validity, and refer back to the theories on which their observations and recommendations are based. This article provides an overview of two of the group devel-opment theorists we will feature in our pre-conference work-shop in Montreal: Wilfred Bion and William Schutz. (Though not included here, at the Montreal conference, we’ll also explore group development theories by Banet, and Warren Bennis and Herb Shepard, and the team development model of Alan Drexler and David Sibbett.) Use them to identify what is “Without a strong understanding of group development theory, the practitioner is simply guessing at which tool will be appropriate for a given client situation … leading to unnecessarily limited opportunities for intervention.”
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.640 | 0.465 |
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".