On the psycho-emotional deficitisation of workers in the age of cognitive enhancement
Bibliographic record
Abstract
Despite being the subject of public and scholarly debates for some time, the topic of cognitive enhancement remains theoretically under-developed in organisation studies. This is because the ‘dots’ still have to be ‘connected’ between macro-level phenomena (here, the therapeutic ethos and cognitive capitalism), and micro-level phenomena (in this case, cognitive abilities). In this essay, we use Fromm’s notion of social character to theorise dialectally about the interaction between these macro and micro-level phenomena. Doing so enables us to examine how the macro/micro interaction fosters to adoption of cognitive enhancement in the context of work, and what kinds of consequences might emerge from this. We propose the psycho-emotional deficitisation of workers as a central consequence of the aforementioned interaction, and define it as an internalised version of external ideals of what it means to be a productive worker under cognitive capitalism, which over time generates and reinforces the affective experience of being deficient . Our theorising around socially patterned defects of a cognitive kind has crucial ramification for our understanding of technology-mediated affective control at work and how human–technology interactions shape the subjectivities of workers towards greater self-inferiorisation vis-à-vis the perceived superiority of technology. We close by foreshadowing avenues for future research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".