Little Teams, Big Data: Big Data Provides New Opportunities for Teams<i>Theory</i>
Bibliographic record
Abstract
Over the past 25 years, industrial and organizational (I-O) psychologists have made great strides forward in the area of teams research. They have developed and tested meso-level theories that explain and predict the behavior of individuals in teams and teams operating within and across organizations. The continued contributions of I-O psychologists to theory and research on teams require us to address the challenges—several of which were well described in the focal article (Guzzo, Fink, King, Tonidandel, & Landis, 2015)—and embrace the opportunities that are being ushered in by big and broad data streams (Hendler, 2013). We suggest that a principal unique value add of the I-O psychologist to the basic scientific endeavor of understanding small teams comes in the form oftheory—theories that explain why, when, how, and to what end individuals form relationships needed for teams to function in unison toward the accomplishment of collective goals. Some have argued that the big data revolution means “the end of theory,” suggesting petabyte data render theoretical models obsolete (Anderson, 2008). On the contrary, we submit that big-data enabled social science holds the promise of rapid progress in social science theory, particularly in the area of teams.
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.019 | 0.041 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".