Bibliographic record
Abstract
Palgrave Macmillan , New York , 2009 , ISBN: 978-0230611733 , hardback, £45 , pp. 248 . This book is an empirical look inside the world of R&D co-workers, where individuals choose to share and not share knowledge with one another. It is a rigorous examination of the social mechanisms that come into play in the decision to voluntarily communicate scientific know-how with someone inside one's own firm. The book is noteworthy for its revelations into the darker side of interactions among scientists – the causes and rationale for denying a colleague's request for assistance. The book is multi-method in its approach, combining both survey and open-ended responses. The book offers insights for both scholar and practitioner. The practitioner will want to turn their attention to the introductory chapter, which provides a clear summary of findings, many of which are actionable. In fact, both academic and R&D manager would be advised to read the foreword to the book crafted by Melissa Appleyard, who laid the groundwork for this study (Appleyard, 1996). Her insights alone make this book worth further scrutiny. The book's research design is exemplar; it is both detailed and well structured. R&D work is technologically dispersed, covering a growing array of scientific disciplines with often increasingly deeper and broader bases of knowledge, while at the same time, R&D activity may be physically quite scattered across the globe. Ensign models the entrepreneurial decision to share or not to share knowledge as an event that is situated in time where consideration of past and future play a role. Reputation is seen as comprising past behaviour and expected action. The incident of sharing or not sharing with a fellow R&D scientist within the firm is also situated realistically. A potential recipient of knowledge looking for assistance with a basic research problem is more likely to find the holder of knowledge willing to share than a potential recipient seeking assistance with a technological problem that is much closer to commercialization. While we have responses to past events and future expectations in Ensign's book, getting both sides of the dyad or a wider swath of interactions in the network would be instructive – particularly if they were recorded as events unfold, as exchanges build off one another and reputations ebb and flow. Ensign himself notes that it would be worthwhile to look at the role of these social mechanisms of governance in situations involving the exchange of tangible assets – where a contract might suffice. Transmission of knowledge is often not simply a dichotomous event; there are gradients to success and effort taken. While Ensign explored situations in which technological expertise was sought informally, which may result in meaningful intellectual collisions and two-way sharing, it is probable that creative sparks also fly when knowledge is shared without a request. This too might warrant our scrutiny.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".