The `boundaryless' career and career boundaries: Applying an institutionalist perspective to ICT workers in the context of Nigeria
Bibliographic record
Abstract
Drawing from institutional theory, this article explores `boundaryless' careers and the nature of career boundaries in the information and communication technology (ICT) industry in Nigeria. The specific objectives are to explore: 1) whether career mobility in Nigeria reproduces or challenges contemporary projections of the `boundaryless' career (i.e. as characterized by increased inter-firm mobility) and 2) the structural boundaries (barriers) that constrain individuals' ability to enact the boundaryless career in this context. Findings of the interviews with 50 technical professionals in the Nigerian ICT industry challenge contemporary projections of `boundaryless' careers by providing evidence to support the continuing existence of career boundaries and traditional career patterns (i.e. as characterized by hierarchical and progressive movement within a single organization). Findings also suggest that ethnic allegiance, personal connections, gender discrimination, perceptions of educational qualifications and the nature of work biography constrain individuals' ability to enact the boundaryless career in the ICT industry. Overall, the article contributes the Nigerian perspective on boundaryless careers and career barriers to the growing consideration of career phenomena in different national contexts.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".