Innovation Climate Profiles
Bibliographic record
Abstract
Abstract: While person-centered analyses can provide information on important configural effects in the context of multi-dimensional constructs, we have found little research taking this approach with innovation climate. Specifically, we found no research applying this approach to results from the Team Climate Inventory, the most frequently used questionnaire on innovation climate. Therefore, we explore the presence of latent profiles in responses to the Team Climate Inventory and extend this exploration to associations between the profiles and self-reported innovative behaviors. Latent profile analyses conducted on two samples of 435 and 461 participants indicated the presence of three innovation climate profiles respectively indicating low, medium, and high scores on innovation climate dimensions. Innovative behaviors covaried with profiles accordingly. The multi-group analysis supported the similarity between latent profile solutions across samples. We discuss the apparent lack of potential for configural effects and invite researchers who could be interested in interactive effects between climate dimensions to verify whether the configurations implied by their interactions are actually present in the data.
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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 | 0.006 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".