Instrument Development “Intention to Stay Instrument” (ISI)
Bibliographic record
Abstract
Quite a few instruments exist in literature to measure the concepts like absenteeism, attrition, organizational member’s intention to leave and retention. While, there is always confusion between the variable’s and its appropriateness when contextualize the topic to various industries, sectors and regional applications. These variances evidently may observe when an instrument developed in the west and apply it in east to get its validity and reliability. In this context, an instrument developed to measure the causative factors of ‘member’s intention to stay’, especially focused on individual and organizational factors in the manufacturing sector. The instrument development process was initially followed the qualitative research method. Techniques like content analysis, personal interviews with the organizational members, focus group discussion and Delphi technique were adopted. After identification of the variables through Delphi, these variables were exposed to validity and reliability test. Further, content, construct and face validity was made on the sub factors and items generated in the instrument. The instrument was finalized with 76 items under 21 sub factors of ‘member’s intention to stay’.
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 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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".