Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As SARS-CoV-2/COVID-19 travels the planet, we’re sitting at home on the sofa doing our bit, participating in a biopolitical experiment of global proportions. Under the tutelage and watchful eye of population experts – epidemiologists, public health officials, etc. – we and millions more are invited, cajoled and sometimes compelled to act. Today’s most urgent task, we are told, is to ‘flatten the curve’. But what is this curve? On first inspection, it’s a simple plot of the number of new cases of COVID-19 occurring over time, an epidemiological rendering of the movement of SARS-CoV-2 through a population. There isn’t just one curve, of course, but many. Here in Toronto, we receive daily accounts of curves for our city, our province and our country. We’re invited to scrutinise our curves and compare them to those of other populations. We strive to avoid Italy’s fate. Can we reproduce South Korea’s relative success? Crucially, these curves are not simply accounts of the past, but also depictions of possible futures. Our trajectories have partly been set, we are told, but we can – indeed must – write our story’s ending. Through apposite collective actions, we can flatten the curve, bend it towards a future where our healthcare services are not overwhelmed, thus saving as many lives as possible. We are both subjects and objects of the curve. Operating the curve depends on numbers, yet we are simultaneously drowning in and parched for them. On the one hand, modellers generate unending numeric projections that include staggering mortality rates. These potential numbers are overwhelming. On the other, simple acts of counting have proven deeply problematic and we’re flying blind. We don’t yet know with any accuracy how many are or have been infected, where they are, who and where their contacts are, what the case and infection fatality rates are. Nor are our few existing metrics standardised, making comparison tricky. We must treat numbers – ours and others – with suspicion. Still, the only way to trace our curve, to hone our model projections, to defy our models’ worst predictions, and to escape our domestic incarceration, is to collect such numbers. And to share them. The only way to flatten the curve, it seems, is to show, know and act upon it collectively. For this, the power of numbers to illuminate and bind is vital. In time, anthropologists will have plenty to say about SARS-CoV-2/COVID-19. Even now, though, while working together on our curves, future directions beckon. One is to rediscover in biopolitics not just bare life and the politics of death, but the productive regulation of population; those instances, as Foucault famously put it, when power operates to ensure, sustain and multiply life. Then come numbers, counting, metrics, standardisation and quantification: all things anthropologists often love to hate. True, these things can seduce, reduce and deceive. But they can also foster life and enhance our ability to work on it. Taking numbers and their life-sustaining capacity seriously wouldn’t go amiss. Our lives may depend on it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it