Fundamentals of Information Technology By Sunny Handa (Markham: LexisNexis Canada Inc., 2004)
Bibliographic record
Abstract
In the early 1990s, I purchased my first stereo with a CD player. I found myself trapped in a conversation with someone who tried to convince me that it was utter folly not to buy a turntable, because CD technology simply couldn’t replicate the ‘‘warmth’’ of vinyl. Had I only Handa’s book to hand, I could have provided a straight- forward and understandable explanation for why my records were well enough left in my parents’ basement; although ‘‘digitization . . . fails to record all characteristics of analog data, even at the highest finite sampling rate . . . Complete pinpoint accuracy is not necessary, [because] we ‘hear’ over the gaps’’. This explanation was not likely to win the argument with that particular person, but nonetheless, it is completely apt.\nHanda’s 152-page book does exactly what it sets out to do, provide ‘‘a discussion of technology beginning with the most basic concepts’’. It includes sections (and this summary is not comprehensive) on the fundamen- tals underlying modern technology (analog world, digital world), computers (hardware, software), communications networks (transmission modes, speed, technologies, the nature of content, its production and distribution), the Internet (ISPs, e-mail, WWW, file sharing, domains, e- commerce, and geographical screening), standards (development, benefits and pitfalls of) and cryptography and security.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.117 | 0.103 |
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".