Toward better understanding developmental reflection differences for use in management development research and practice
Bibliographic record
Abstract
Purpose We introduce a new approach to developmental reflection in which the focus is on differences in how people reflect. When reflecting on challenging experiences, people achieve better development when they tend to look for causes of what happened within changeable personal characteristics, and they subsequently focus on the improvement of those personal characteristics. Design/methodology/approach Supervisors and subordinates with leadership responsibilities in diverse jobs in varied industries provided survey data (444 individuals in a psychometric testing sample, and 419 paired subordinate/supervisor dyads in a model-testing sample). Findings The reflection difference construct had the expected factor structure, reliability, and was distinguishable from eight conceptually related variables in the literature. Reflection differences were predicted by the theoretically relevant job, person, and situational variables and were associated with development and performance outcomes. Practical implications The reflection construct might be used for prediction to identify the individuals who are likely to get the most from challenging experiences and improve. Further, by identifying predictors of reflection, ideas for enhancing reflection are provided. Also, by uncovering specific underlying dimensionality of reflection, this offers specific targets for interventions beyond generally encouraging people to reflect. Originality/value This study establishes support for: (1) the new theoretical framing of reflection differences, (2) a new preliminary model of antecedents and outcomes, and (3) an initial scale for future research and practice that can be more explicit about understanding and addressing underlying differences in how people reflect.
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 | 0.100 | 0.239 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".