If you love her, (don’t) let her go: generative “holding back” and the de-containment of the <i>Nancy Drew</i> PC games
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
In 2015, disaster struck for HerInteractive's Nancy Drew PC game franchise when the company delayed the release of Midnight in Salem, the 33rd game in the series. When the game was finally released, many fans lamented the loss of the so-called “classic Nancy Drew” atmosphere. One notable object through which these discourses cohered was HerInteractive's proprietary game engine, which had been used since 1998, and which was now being abandoned in favor of Unity. Through an analysis of the Nancy Drew games' former proprietary engine (in its appearances within fan and company discourses as well as its deployment in situ within the first 32 games of the series), I argue for this object as a container technology which mobilized a technique I term “holding back” to contain the culture and identity of the classic Nancy Drew games. I propose “holding back” as a strategy of resistance which articulates alternate imaginaries generatively out of step with the so-called progress of technology, and argue that the proprietary game engine cohered the world of the classic Nancy Drew games by desynchronizing players and games from trends within the mainstream gaming industry, thus carving out space for counterhegemonic play for alternative gamers.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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