Bibliographic record
Abstract
mission is to organize the world's information and make it universally accessible and useful That's the first sentence their website's Corporate Overview. mean it. And by any measure they're getting there. A metered study by Nielsen/NetRatings July 2006 measured the web search behavior of 500,000 people worldwide and found that 49.2% of their searches were done by Google. Runners up were Yahoo (23.8%), Microsoft (9.6%), AOL (6.3%--they use Google's search engine), and Ask.com (2.6%). A year Later, Netapplications gave Google 55% of the worldwide searches, when Google UK and Google Canada were included. The googling of the web is a story as revolutionary as the emergence of the web as a commercial tool. Amazingly it was just nine years ago that two Stanford Ph.D. candidates Larry Page and Sergey Brin, incorporated their Silicon Valley garage operation, whimsically naming it for the mathematical term googol, ten followed by 100 zeros. The name reflected their unique approach to search--look at every word of every page the whole World Wide Web, not just the webpage title. Their trick for doing that was to use as many parallel processors as it would take. (Google won't say how many processors it uses today, but published estimates range from 175,000 to in excess of 450,000.) About these arrays of computers at data centers around the world, one observer huffed: They are so primitive I wouldn't give one of them to my son for his high school work. But they do the job. That is, first, to crawl through every webpage and capture its contents. Then the computers compile an index of all the elements. In 2001 Google patented PageRank, their unique system for judging how closely each found website matches the search query. The system delivers to the searcher a List, with snippets of contents, of dozens, hundreds, tens of thousands, or millions of websites that best match the search query. Then it ranks them the order of their likely match to what the user intended. To determine what most closely matches the user's intention, the system analyzes the words and content of each page, using an algorithm with more than 500 million variables and two billion terms. PageRank weighs the vote of each page's intrinsic relevance, apart from the specific words the search query. The whole process is automatic and normally takes less than half a second. The strategy has obviously paid off big time. Its second quarter report showed $3.87 billion revenues, up 58% from the previous year; and $1.22 billion operating income (29% of revenues, 4% Lower than first quarter 2007). The firm had $12.5 billion cash on hand as of June 30, 2007, and 13,786 employees. Where did all that money come from? A big share of profits comes from advertising. Two closely meshed programs, called AdWords and AdSense, drive Google's ad revenues. The program's goal is to attract leads and turn them into sales at the Lowest cost. For some time, advertisers have known that digital advertising does this better than alternative marketing media. In his 2006 book The Search, John Battelle cites these customer-acquisition figures from Piper Jaffray: $8.50 per customer with search; $20 with yellow pages; $50 through online display ads; $60 with e-mail; and $70 with direct mail. Using AdWords, an advertiser creates text ads for placement throughout the web where they will Likely attract the most qualified prospects. Those digital ads are worded to motivate that prospect--then Looking at another webpage--to click on the Google-enabled ad there and be transported to the advertiser's webpage. What magic words have the best chance of doing that? Google answers: put words the ad that match the search words or the content of the page where the prospect went an unrelated web search. The advertiser can put one ad as many such words as she wants to. Since Google knows all the words all the webpages, it's a good position to suggest the matching words--or even a whole text ad. …
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.055 | 0.046 |
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".