Bibliographic record
Abstract
How do you get to grips with an early American poem? A good toolbox of critical approaches and perspectives will include formal analysis, material texts, cultural work, race and gender, reception and reading practices, together with an inquisitiveness about the various ways in which a poem makes connections. It also helps to know some of the key uses to which poetry was put by English-speaking colonists in the seventeenth and eighteenth centuries. Worn pages show Puritan readers using devotional poems to support their daily piety in early New England. Manuscript elegies offered consolation to bereaved family members, and published broadsides shaped the values of the wider community. Commemorating a public figure could enable a socially marginalized writer, such as Phillis Wheatley, to find an authoritative poetic voice. Epistolary exchanges of poems among coteries allowed some educated eighteenth-century women to pursue their friendships and intellectual development despite being barred from public careers. Throughout the period, allusions ranging from homage to parody, illustrate the transatlantic adaptation of British genres and styles to American circumstances. In the revolutionary period, anonymous and ephemeral newsprint poetry whipped up patriotic feeling, while a handful of poets published their work in elegant volumes.
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 | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".