World Leadership Survey Biannual Report on Employee Commitment and Engagement 2013–2014
Bibliographic record
Abstract
" From the Executive Summary: ""The purpose of the World Leadership Survey (WLS) is to provide a window into how professionals, managers, and executives view their life within the organization. This view of the employee experience will help leaders of organizations understand what employees experience, and what the organization can do to improve commitment and reduce turnover. The good news for organizations in the United States and Canada (the sample for this report) is that respondents are mostly committed to their organizations, satisfied with their jobs and their pay, work more than the typical 40-hour workweek, and do not currently intend to leave their jobs. The professionals, managers, and executives surveyed feel supported by their organization and by their direct supervisor, and think that their organizations are economically stable. Unfortunately they also feel overloaded, with their work disproportionately interfering with the rest of life, and that there is a high level of political behavior within their organization. Both overload and overt political behavior can reduce individual and organizational effectiveness. This report describes the current employee experience, and what organizations can focus on to maintain and improve commitment and engagement."
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".