19. 1867 and All That . . . : Teaching the American Survey as Continental North American History
Bibliographic record
Abstract
Now that we have learned so much and unlearned, perhaps, even more-shedding nationalist frameworks and cultural myths on our way to an analytically powerful form of transnational scholarship-we are left with a question: How much of this can we share with our students?Specifically, how much can we productively share with students in our introductory surveys?"Why, all of it!,"cries the good teaching angel on one shoulder."For heaven's sake, they can hardly keep straight the simple story, must I really complicate it?,"sighs the tired teaching angel on the other.In this chapter we will try to heed both voices, positing ways to bring new frameworks into our survey classrooms without stretching the canvas, and ourselves, too thin.The scholarship of Nora Faires, Dirk Hoerder, and the many contributors to this volume makes clear that a continental perspective requires thinking at once bigger and smaller.The need to think bigger is readily apparent in any number of ways: rather than focus only on the British colonies and on the United States as it forced its way east to west, we must consider Canada, Mexico, and the Caribbean.Rather than begin with European settlement in North America, we must explore the expanse of time in which the land was known only by First Peoples.Rather than only English-language sources, we must work with French, Dutch, Spanish, and Russian, as well as with indigenous American languages and with anthropological sources.But these scholars are also adamant that we must think smaller: we must set aside the baggy term "Indians" and attend to diverse tribes; we must look inside Spain and see Andalusia, Catalonia, and Galicia; we must look within individuals to view their myriad allegiances to polities and cultures that are themselves both larger and smaller than nations.In that movement from large to small
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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 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".