Predicting Individual Research Productivity: More than a Question of Time
Bibliographic record
Abstract
Despite professors’ education and socialization and the significant re- wards they receive for research activities and output, the 80/20 rule seems to apply; that is, there exists a system of stars who produce a disproportionate volume of research such that most research tends to be undertaken by a small percentage of the academy (Erkut, 2002). Although a growing body of research seeks to address this imbalance, studies of research productivity have tended to reveal its institutional and non-behavioural antecedents. As a result, there exists very little re- search that considers the strategies that individuals employ to improve their personal research productivity. This exploratory, questionnaire- based study of a sample of Canadian professors attempts to address this gap by examining the relationship among a number of strategies, what professors report as being their average annual number of publications over the past five years, and their perceptions of their level of research productivity. Not surprisingly, in this study, we found that the amount of time that individuals invested in research activities predicted their level of research productivity. Additionally, strategically focusing one’s research positively influenced journal publication levels, both directly and through its interaction with seeking resources (such as research grants). A strategic focus also positively predicted self-perceived re- search productivity through its interaction with managing ideas. Finally, although the perceived need to free up time from teaching and committee work was negatively related to journal publication levels, it was positively related to perceptions of productivity. Malgré l’importance accordée à la recherche par les universités, la règle des 80/20 s’applique toujours : la majorité de la recherche est menée par une minorité de professeurs (Erkut, 2002). Les études antérieures sur la productivité des chercheurs se sont surtout concentrées sur les antécédents institutionnels, accordant peu d’attention aux stratégies individuelles visant à améliorer la productivité personnelle. Cette étude exploratoire vise à répondre à cette lacune. L’enquête révèle que le nombre d’heures qu’un individu investit dans les activités de recherche est un prédicteur du niveau de productivité. De même, l’adoption d’une stratégie de focalisation des recherches est associée à un plus haut niveau de publication dans les revues scientifiques, en particulier pour les chercheurs qui ont également fait des recherches de financement (par exemple pour des subventions de recherche). En outre, l’interaction entre la focalisation stratégique des recherches et la gestion des idées favorise la perception de soi-même comme un chercheur productif. Enfin, le sentiment de devoir réduire le temps d’enseignement et de service à la collectivité s’est avéré négativement corrélé au niveau de publication dans les revues scientifiques, mais positivement corrélé à la perception de soi comme chercheur productif.
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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 | 0.059 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.060 | 0.064 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".