The Biography of an Algorithm: Performing algorithmic technologies in organizations
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
Algorithms are ubiquitous in modern organizations. Typically, researchers have viewed algorithms as self-contained computational tools that either magnify organizational capabilities or generate unintended negative consequences. To overcome this limited understanding of algorithms as stable entities, we propose two moves. The first entails building on a performative perspective to theorize algorithms as entangled, relational, emergent, and nested assemblages that use theories—and the sociomaterial networks they invoke—to automate decisions, enact roles and expertise, and perform calculations. The second move entails building on our dynamic perspective on algorithms to theorize how algorithms evolve as they move across contexts and over time. To this end, we introduce a biographical perspective on algorithms which traces their evolution by focusing on key “biographical moments.” We conclude by discussing how our performativity-inspired biographical perspective on algorithms can help management and organization scholars better understand organizational decision-making, the spread of technologies and their logics, and the dynamics of practices and routines.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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