Learning by hiring or hiring to avoid learning?
Bibliographic record
Abstract
Purpose – The purpose of this paper is to advance the understanding of the mechanisms associated with learning-by-hiring. Design/methodology/approach – The authors built a yearly dyad data structure of all of the hiring and sourcing firms in the US biotechnology sector between 1973 and 1999. Findings – The authors found that hiring firm’s learning from a prior employer’s knowledge is limited in scope to the knowledge developed by the newly hired inventor, and could be attributed to new hire direct involvement. Learning from new recruit occurred only when incumbent inventors collaborate intensively with the hired inventor. Accordingly, what might seem like learning-by-hiring may result in hiring to avoid learning, unless the organization creates the social structures that facilitate the exchange of knowledge within and throughout the organization. Practical implications – The results, thus, highlight the importance of aligning a firm’s social environment with its strategic goal to learn from its external competitors. Social implications – Recruitment is one means by which organizations can interact with and learn from their external environment. Incumbent inventors are more likely to learn from hired inventor knowledge through the development of a collaborative social culture that facilitates communication and trust in the process of transferring knowledge among individuals. The results, thus, highlight the importance of aligning a firm’s internal environment with its strategic goal to learn from its external competitors. Originality/value – The authors suggest that access to new knowledge bases through hiring is not sufficient for learning purposes; internalizing a new hire’s knowledge also requires the internal mechanisms, structures, and cultures that motivate knowledge sharing and promote mutual trust.
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 | 0.017 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".