The Instrumental Self: Proactive Self-Objectification as a Strategy for Work Vigor in the AI Era
Bibliographic record
Abstract
This study integrates Weber's Instrumental vs. Value Rationality, Piaget's Assimilation vs. Accommodation Theory, and The Extended Self Theory (Belk, 1988, 2013). We posit that AI-induced dehumanization pressure (Kim & McGill, 2024) compels employees to adopt Proactive Self-Objectification (PSO), an instrumentally rational (Weber) and assimilative (Piaget) coping strategy. PSO facilitates Functional Self-Extension (FSE)—the incorporation of AI's functional attributes into the self-concept—thereby yielding immediate gains in Work Vigor and Super-Agency. Crucially, this instrumentally-driven path systematically suppresses the relational and learning dimensions required by Value Rationality, leading to the decay of organizational resilience, specifically Learning Loss and Psychological Disengagement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".