Navigating Expectations and Realities: A Phenomenological Study of Graduate Unemployment in Alfred Duma Municipality, South Africa
Bibliographic record
Abstract
In South Africa, graduate unemployment is a growing problem. This paper explores graduates' expectations when they leave universities versus what they encounter in the labour market and how they manage and react to it. The paper uses a qualitative, phenomenological design on a purposively drawn sample of 24 South African graduates from the Alfred Duma Local Municipality. Data were analysed using thematic analysis on Atlas.ti. The paper further applies a working hypothesis approach to draw testable hypotheses from the data. The study made the following thematic findings: Theme 1: Unemployed ADLM Graduates’ Disillusionment with Employment Transitions; Theme 2: Perceived Sources of Graduates’ Unmet Expectations; and Theme 3: The Sociopsychological Impact of Graduate Unemployment on Youths. In an integrated form, the themes show that graduates managed expectations versus reality gaps (further complicated by community expectations) by altering expectations. Still, failures to reconcile expectations versus realities came with psychological adversities among some. The paper advocates for individual psychological capital development and realistic expectations set by universities, alongside macro-level policy reforms like mandated internships, entrepreneurship and retraining grants, and unemployment benefits. The study proposes a working hypothesis model on how graduates can actively manage their job expectation-reality gap. This is key for future research, offering a testable model to predict graduate responses and inform interventions for unemployment.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".