Globalisation, Working Conditions, Cheap Labour and Employment Relations in Kenya
Bibliographic record
Abstract
People perceive globalisation differently. Some consider it to be the internationalisation of local economies in terms of trade, foreign direct investments, agriculture, technology transfer and dominant culture, amongst others. However, globalisation, with its liberalisation and deregulation policies, seem to have created additional turmoil in the workplace as far as employment relations is concerned. The main objective of this paper was to investigate how globalisation has influenced employment regulations/deregulations in Kenya. The study adopted an explanatory mixed method approach. About 500 closed ended questionnaires were distributed to employees of the sampled companies, and of these, 483 were satisfactorily completed, which culminated in a 97% response rate. In addition, 10 key employment relations stakeholders were interviewed for the qualitative phase of the research study. The study revealed that the conditions of workers, in terms of health, have improved in Kenya since globalisation. It also reflected that working conditions, particularly regarding safety, have improved since globalisation. The study further showed that organisations in Kenya are exploiting children, who are part of their unskilled workforce, by paying them low wages, which reflects a recent, rising trend in the use of child labour in Kenya, particularly in manufacturing sectors. Thus, the study's findings show that there has been an increase in the use of cheap labour amongst Kenyan organisations. In addition, the study indicates that Kenyan companies favour foreign employees compared to local ones in terms of salaries and wages.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".