Bibliographic record
Abstract
I wanted to get acquainted a bit more closely with the London masses, to study not abstract man as he figures in the columns of statistical tables, not the ‘hands’ bringing a known portion of their labour power to market, but rather Smith, Jones, Clark and Robinson, who have their own private joys and their own private sorrows. I wanted to see what this Smith was like in himself: the one who is clocking his ‘hours’, getting his wage on Saturdays and… has such high chances of becoming the pensioner of a workhouse in his old age. In a word, I was interested in ‘ living numbers’, to use Gleb Uspensky's expression. ‘Facts by themselves do not say anything; they teach nothing until interpreted by reason’, says Marshall. And in order to interpret correctly, we need to reckon not with an abstract Smith, but with a live one who has his own individuality. But how could I make my wish come true? Of course, by following the example of a hundred other English observers and settling down myself for a time in some poor quarter of the huge metropolis. I had often made short forays there; but they have the disadvantage of not revealing to the investigator a picture of everyday life. It is quite another matter to live oneself within the field of observation, if one can put it like this. But which quarter to choose? Whitechapel? North Pancras? Lambeth? All these are centres of the utmost destitution. They are interesting in their way, but firstly, there already exists a large literature dealing with them. Dozens of researchers have worked and are currently working there. Secondly, although these quarters may present a striking and vivid picture, it will not illustrate the life of young England, called into civic life by the latest reforms; and it was precisely this that interested me. In view of this, I decided to take up residence in a quarter with a mixed population, where alongside the Slums there would also live people with regular earnings.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.406 | 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; both teacher heads agree on what is shown here.
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".