Predictive models can lose the plot. Here's how to keep them on track
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
Organizations are increasingly turning to sophisticated data analytics algorithms to support real-time decision-making in dynamic environments. However, these organizational efforts often fail—sometimes with spectacular consequences. \n \nIn 2018, real-estate marketplace Zillow launched Zillow Offers, an “instant buyer” arm of the business that leveraged a proprietary algorithm called Zestimate, which calculated the estimated sales prices of real estate. Based on these calculations, Zillow Offers planned to purchase, renovate, and resell properties for a profit.1 While it had some success for the first few years, the model failed to adjust to the new dynamics of a more volatile market in 2021. Zillow lost an average of $25,000 on every home they sold in the fourth quarter of 2021—resulting in a write-down of $881 million.2 This is an instance of what we call algorithmic inertia: when organizations use algorithmic models to take environmental changes into account, but fail to keep pace with those changes. Here, we explain algorithmic inertia, identify its sources, and suggest practices organizations can implement to overcome 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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