Bibliographic record
Abstract
I had landed in America at the moment of what is known in Canada as “the great scare”—that is, the Fenian invasion at Fort Erie. Before going South, I had attended at New York a Fenian meeting held to protest against the conduct of the President and Mr. Seward, who, it was asserted, after deluding the Irish with promises of aid, had abandoned them, and even seized their supplies and arms. The chief speaker of the evening was Mr. Gibbons, of Philadelphia, “Vice-President of the Irish Republic,” a grave and venerable man; no rogue or schemer, but an enthusiast as evidently convinced of the justice as of the certainty of the ultimate triumph of the cause. At Chicago, I went to the monster meeting at which Speaker Colfax addressed the Brotherhood; at Buffalo, I was present at the “armed picnic” which gave the Canadian Government so much trouble. On Lake Michigan, I went on board a Fenian ship; in New York, I had a conversation with an ex-rebel officer, a long-haired Georgian, who was wearing the Fenian uniform of green-and-gold in the public streets. The conclusion to which I came was, that the Brotherhood has the support of ninety-nine hundredths of the Irish in the States.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.456 | 0.214 |
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".