Bibliographic record
Abstract
Even the field's lead ers admit that orga ni za tional devel op ment (OD) has had problems adapt ing to the need for better approaches to man ag ing change.For exam ple, Burke (1997) observed that OD prac ti tio ners stood on the side lines and watched while new man age ment tech niques were being intro duced.In a review of the sociotechnical sys tems (STS) tra di tion, Mathews (1997) con cluded that there were prac ti cally no exam ples of com pa nies that had cho sen STS over com pet ing approaches such as business pro cess reengineering.Around the world, busi ness pro cess reengineering was the choice for firms intend ing to trans form their work pro cesses through the use of informa tion tech nol ogy (IT).Although we can all agree that the reengineering approach was flawed in some respects, it behooves OD prac ti tio ners to crit i cally exam ine why their con ven tional frame works and meth ods lost out in the mar ket place so com pletely.As Mathews noted, more effort was spent by pro po nents of STS on ideo log i cal contests than on devel op ing sound meth od ol o gies and pro ce dures that would have taken STS into the main stream and linked it with IT inno va tions.380 Nicolay Worren is a con sul tant at Andersen Con sulting's Oslo office.He is cur rently on a pro j ect at the firm's Insti tute for Stra te gic Change in Boston.
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 | 0.015 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.106 | 0.088 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".