Bibliographic record
Abstract
For Andzani, home has always been a trigger for unpleasant memories, it has become the site for anxiety. After completing his Accounting Degree at the University of Cape Town and securing employment after, Andzani minimizes his visits back home to evade those memories home allows to seep through and confront him. He fears what this remembering will do to him, undo in him. Then one morning he receives a phone call from his uncle, Sontaga, to come fetch his mother, Violet, and take her to a mental institution because her mental health is deteriorating. As if given a last chance, on this trip, long-repressed memories flood his head and dull his days in order to force him to pay attention to them, digest them. In Dorothy L. Pennington conceptualisation of memory as a helix, she states that “the past is an indispensable part of the present which participates in it, enlightens it, and gives it meaning.” Taking this assertion as a point of departure, ‘A Soft Landing' is a novel that explores the implications of a past not decisively dealt with. The novel explores how the past gives meaning to present identities and how new identity formations are negotiated within the eye of the past participating in the present.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.135 | 0.032 |
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".