Bibliographic record
Abstract
Recent research suggests that, as consumers feel that their choices are restricted, many respond by turning away from the market leader. It is widely assumed that in many technology markets, dominant players have a powerful advantage and often are able to leverage that edge over time. But this is not necessarily true. Over the past decade, popular social networking sites including Friendster, MySpace and Bebo initially picked up a large number of users only to lose ground to new competitors and fade into the background. Facebook, by contrast, has succeeded at dramatically expanding its position in the global market, even as it has worked to manage an increasing number of dissatisfied users. Similar patterns of emergence, growth and dominance, followed by consumer disenchantment or ambivalence and a loss of brand equity have affected well-known technology companies such as Microsoft and AOL. Why do companies move from market strength to vulnerability?
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.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".