Bibliographic record
Abstract
In this article, I argue for the position that self-identity is a function of a good motivational model. Employee motivational models have a bearing on organisational performance and growth. While I am aware that various motivational models influence employee performance in the workplace, my view is that not enough education has been provided for employees to understand how their performance can be further enhanced. This article therefore presents propositions of a self-identity motivational model as a theoretical model. The propositions were developed from a study that adopted a pragmatic paradigm and a mixed methods research approach and a case study research design. The main purpose of the study was to investigate employees’ perceptions of their motivational models at selected government primary teacher-education colleges in Zimbabwe. Convenience and purposive sampling methods were used in selecting three primary teacher-education colleges and eleven lecturers. For triangulation purposes, document analysis, open-ended questionnaire, reflective journals, semi-structured face-to-face interviews and focus group discussion were used as data generation/production instruments. Emails and zoom platform were used since the study was carried out during Covid-19 era. Data that were gathered were analysed through guided analysis and a thematic approach. Furthermore, in order to ensure trustworthiness, issues of dependability, confirmability, credibility and transferability were considered in this study. In addition, ethical issues were employed, such as use of consent letters, anonymity and withdrawals. The study concluded that, employees ought to develop self-identity that would help them to become self-actualised; and become permanently motivated even beyond the workplace.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".