Striving for Excellence: Organizational Climate Matters
Bibliographic record
Abstract
Objective – To describe steps undertaken by the University of Connecticut Libraries to respond to the results of an organizational climate assessment. More than 80% of the Libraries’ staff members completed the ClimateQUAL® survey instrument in the spring of 2007. An organizational development consultant designed a format for focus groups to provide anonymous, but more detailed, experience-based information to help the Libraries discover, understand, and respond to the root causes of “problem” areas indicated by the survey results. Methods – In November 2007, the consultant conducted five 90-minute, on-site focus group sessions, each with 7-15 participants. Two of the sessions were open to all staff members, while the others focused on underrepresented minority group members, team leaders, and the staff of one specific team. Results – A summary report based on compiled data and including recommendations was submitted and discussed with the Libraries’ Leadership Group. In line with organizational development practice, recommendations were made to engage those closest to the “problems” (i.e., the staff) to design and recommend improvements to internal systems. The consultant advised the formation of six teams to address internal systems, and an initial three teams comprised of staff members from across the library were formed. These teams were charged with formulating a set of recommended actions that will contribute to a healthier organizational climate in three areas: leadership and team decision making; performance management; and hiring, merit, and promotion. The findings, recommendations, and progress-to-date of each team are summarized. Conclusion – The ClimateQUAL® results and the follow-up with the organizational development consultant helped in identifying potential problem areas within the Libraries’ internal systems. The consultant made recommendations that led to the development of concrete roadmaps, benchmarks, and associated strategies. The Libraries’ progress on its strategic plan will serve as the barometer for gauging the effect of these changes.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".