Bibliographic record
Abstract
Big Fish raises the hermeneutic question how narrative truth relates to factual truth—how “what is said happened” relates to “what really happened.” Will wants to know facts about his father’s life. His father, Edward, is dying but he never accedes to his son’s request for a factual autobiography, preferring to tell stories about the significant moments in his life and the people he encountered. Narrative truth is what Edward values, for it opens up the dimension of significance— “what an event means to the narrator.” The meaning of events narrated is dramatized in this film. Big Fish is a story about redemption and transformation. Everyone whom Edward encounters is redeemed or changed in a positive manner. Even his son, Will, is changed. Over time, he comes to see the value of story and vows to portray his father’s life the way he wanted it told. Big Fish poses hermeneutic problems on two levels. On the individual level, the conflict between narrative and factual truth arises when individuals seek to authenticate stories told by aged relatives or when therapists attempt to interpret accounts told them by patients. On a cultural level, the narrative versus factual truth issue is experienced by scholars puzzling over the historicity of ancient religious narratives. The importance of this issue is illustrated in relation to narratives about Abraham, Moses and Jesus. Not only do these texts stand at a distance from the events they purport to describe, we, too, as interpreters, are situated at a distance from their time of writing. The question arises, can we now move from narrative truth to factual truth (historicity)? If so, how? It is argued that with respect to ancient cultural historical narratives, we cannot now “get behind” the narratives to corroborate the historicity of actions and sayings. In contrast, Big Fish portrays Will ascertaining details about his father’s story by hearing the account of his birth firsthand, finding records and in speaking to people still alive in Specter, a town transformed by his father’s actions. In a sense the film “cheats,” that is, it portrays what is often not possible— historical corroboration—with respect to personal, therapeutic or scholarly hermeneutic situations. It is therefore contended that the film should have ended just with Will’s hearing the stories. That would have placed him squarely in the hermeneutic quandary.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| 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".