To what extent is the skills shortage debate instrumentally informing policy makers in the private sector?
Bibliographic record
Abstract
Using a qualitative approach, the thesis explores comparatively whether the skills shortage debate in Zimbabwe and Canada is influencing private sector recruitment and training policies. Though, numerous scholarly works have explored the skills shortage debate, the extent to which the debate is instrumentally informing policy makers to enable them to develop effective recruitment and training policies remains unclear. The thesis aims to address this gap, as current literature does not clearly reveal the extent to which private sector policy makers are aware of the debate or even paying attention to the debate when developing their polices.Though, the skills shortage debate cuts across various sectors, information and communications technology (ICT) positions were chosen as the focus of the study because the debate is prevalent in the sector.The case study research design is applied to reveal policy makers’ perceptions on the topic. 20 interviews with private sector human resources management executives and documentary evidence were used for triangulation purposes. The thesis relies on human capital theory and institutional theory, amongst others, to enhance understanding of the labour market processes and explain the relationship between the skills shortages debate and private sector recruitment and training policies in the two countries. The theories facilitate conceptualisation of the causes of skills shortages in the labour markets.The main finding of the study is that the skills shortage debate is not directly influencing recruitment and training policies in the private sector. The skills deficit debate was not instrumentally informing policy makers in the private sector because there are barriers that are deterring the knowledge transfer between industry and academia. There was limited interaction between academia and industry which deterred the debate from influencing policies. Private sector executives were not paying attention to academic research because of lack of confidence in the education system and in some instances, a perception that institutions of higher learning were slow at adapting to the evolving changes. The findings in Harare and Montreal showed that the policy formulation process was influenced by other multiple external and internal contextual factors confronting the organisations. The skills gap debate was not among the everyday realities.
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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 | 0.040 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| 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".