Curiosity signpost: Build our alumni hypernetwork
Bibliographic record
Abstract
From the moment we graduate, we begin a race. We scramble to get ahead, secure our first job, our first promotion. Training for this race begins earlier and earlier. Internships, work placements, job shadowing, and mentorship options abound, all before graduation. These options may have factored during our studies. Sociologist Phillip Brown describes this as the ‘opportunity trap’: ‘Middle-class families are adopting more desperate measures to win a positional advantage,’ Brown argues, ‘They are having to run faster, for longer, just to stand still. Yet if all adopt the same tactics, nobody gets ahead. This is the opportunity trap as few can afford to opt out of the competition for a livelihood.’ Another sociologist, Pierre Bourdieu, calls this ‘playing the game’. In the last chapter, I presented an extended game analogy. In this chapter, we take it one step further to include our alumni capital. Some of us didn't even get the chance to enter the competition. Many of us had work or family caring responsibilities during our studies. Ironically, this left less time to prepare for life after graduation. Maybe we were first in our family to go to university. Or the first person in our friend group to go abroad for university. Or even the first person we know in our community to study a subject. It's not only playing the game but also ‘knowing the game to play’. This book offers a clear response: our advantage is our alumni hypernetwork. This network is hyper as the breadth is so wide, the depth so deep. If, suddenly, all alumni discovered their alumni capital, there is plenty to go around. Our network of alumni opens our world to so many other networks too. When we met, Jessie Cripton set a clear agenda. We connected on the University of Toronto's online community platform. Through sophisticated algorithms, the platform makes studentalumni or alumni-alumni matches. If the matches agree, they can arrange a short meet-up. Cripton recognized, even as a student, the value in connecting with as many alumni as possible. She was first in her family to attend university and wanted to broaden her network
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.101 | 0.052 |
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".