Stringtern: springboarding or stringing along young interns’ careers?
Bibliographic record
Abstract
<p>Young people are repeatedly promised that internships will pave the way to the career of their dreams by providing the ‘hands-on experience’ necessary to differentiate themselves in a fierce job market. However, in many industries, internships – and increasingly unpaid internships – have become the obligatory norm. Young people quickly learn that the internship is not an opportunity, but rather a ‘necessary evil’ that, for many, strings them along in the hope that it <em>may</em> lead to a less precarious paid opportunity. In this article, our findings are based on 12 in-depth interviews with young female interns in the creative industries based in Toronto and New York City. Our participants recognise that in the current economic climate, they need to ‘pay their dues’; however, they often enter into a system of sequential – or string – internships, and become, what we label, a <em>stringtern</em>. In an evolving internship market in North America, we develop a typology of internships including (1) paid/underpaid/unpaid, (2) academic credit/not-for-credit, (3) for-profit/non-profit, (4) full-time/part-time and (5) on-site/off-site to develop a common language to critically analyse the culture of internships. By valuing young people’s perspectives as gleaned from our interviews, the typology aims to provide a more nuanced way to approach the complexity of unpaid internships and the transition from education to the workforce. Furthermore, three interrelated implications of the culture of internships are identified: internship as a free trial, internship as conveyor-belt labour and internship as displacing paid employment.</p> <p> </p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.000 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".