Bibliographic record
Abstract
Although the famous Mughal poet Mir Taqi Mir’s Persian text Z-ikr-i Mīr has come to enjoy considerable renown as the first autobiography penned by an Urdu poet, scholars of Mir have continued to be puzzled by the text’s contents. Its diverse sections comprise a mishmash of hagiography, historical chronicle and popular bon mots, and yield little in the way of informing us about Mir the man or Mir the poet. Even more problematical is the inclusion in the text of numerous ‘facts’ that are quite easily disprovable. Should we then consider much of Z-ikr-i Mīr to be false and fabricated? Or should we take Mir’s intention in composing the text to be something other than autobiographical? This article argues for the latter, proposing that much of what is confusing about the text is only really so because of our misplaced generic expectations from it—many of its ‘inconsistencies’ may be accounted for by freeing it from its autobiographical straitjacket and viewing it instead through the prism of a variety of alternative Persianate genres forming part of a wider, cosmopolitan, classical literary tradition. Through the focal example of Mir’s text as well as examples from a variety of Mughal, Ottoman and Arabic writings, the article underlines the importance of distinguishing between ‘autobiography’ and ‘autobiographical’, and contests notions of the existence of a distinct, recognisable and recognised genre of autobiography in the pre-modern Islamicate context.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".