Attracting and Retaining Knowledge Workers
Bibliographic record
Abstract
A concentration of knowledge workers, including scientists and engineers, has been identified by recent works as an element fostering economic growth in metropolitan areas. The authors’ aim in this chapter is to study the factors influencing the mobility of graduate students in science and technology. The creative class thesis has emphasized the fact that criteria related to the quality of place have a positive impact on the attraction of talents and on economic development. This thesis was the basis for the authors’ research. In this paper, they assimilate the workforce in science and technology to the concept of knowledge workers. The authors compared the influence of criteria related to the quality of place on the mobility of students with other criteria related to career opportunities and to the social network. They collected the data through an on-line questionnaire and they also proceeded to interviews with students in science and technology. The authors present in this chapter the results of their research for Montreal. With a quantitative analysis, they show that while Montreal is often considered as a very attractive place, the criteria related to the quality of place play a secondary role in the attraction and retention of the population studied, while those related to the career opportunities dominate. This leads to nuance the theories that highlight the importance of place versus job opportunities, and shows that while the quality of place may have an influence on the mobility patterns of knowledge workers, job opportunities have more impact on the attraction/retention of this professional category.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".