Discoverability: Toward a Definition of Content Discovery Through Platforms
Bibliographic record
Abstract
Discoverability is a concept of growing use in digital cultural policy, but it lacks a clear and comprehensive definition. Typically, discoverability is narrowly defined as a problem for content creators to find an audience given an abundance of choice. This view misses the important ways that apps, online stores, streaming services, and other platforms coordinate the experiences of content discovery. In this article, we propose an analytical framework for studying the dynamic and personalized processes of content discovery on platforms. Discoverability is a kind of media power constituted by content discovery platforms that coordinate users, content creators, and software to make content more or less engaging. Our framework highlights three dimensions of this process: the design and management of choice in platform interfaces (surrounds), the pathways users take to find content and the effects those choices have (vectors), and the resulting experiences these elements produce. Attention to these elements, we argue, can help researchers grapple with the challenging mutability and individualization of experience on content discovery platforms as well as provide a productive new way to consider content discovery as a matter of platform governance.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.009 | 0.049 |
| Scholarly communication | 0.030 | 0.059 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".