Bibliographic record
Abstract
W asked recently what I am writing about in this series of articles, I explained that each of the last few articles has outlined how the individual investor can apply one step of the six-step investment management consulting (IMC) process in their own portfolio. My colleague was surprised because it is typically affluent institutional or pension fund clients, known to be methodical – almost clinical – in their approach toward money management, who make use of the IMC process. He laughed and asked why an individual investor would want to read a bunch of industry-specific rhetoric on an investment process developed for pension plans. “Why don’t you write on current events and more topical issues of interest? Besides, if they learn these steps and procedures, your readers might not come to us for any further investment advice and counsel.” In retrospect, I must concede that these are good questions, and ones that you may be asking yourself as you read this last article in the series. In my first article in this series (1), I introduced the notion that “the most effective way to circumvent the common mistakes made by an affluent investor is first to establish a prudent process or strategy and then stick with it.” Even the most sophisticated and experienced investors fall prey to common, needless mistakes, but many of these mistakes can be avoided for a more desirable outcome when clearly defined objectives are laid out and a proven process is put into action. Well aware that any investor must navigate through a myriad of decisions to reap the rewards of investing, I introduced the six steps of the IMC process as a framework and theoretical foundation that any investor can use for this purpose. One common mistake is conducting a search for professional money managers to invest your portfolio without first having written an investment plan, or ‘investment policy statement’ (IPS). I have seen investors exert far too much time and energy searching for the money managers who have the best performance numbers without first considering whether that manager’s mandate or style is even appropriate for their portfolio. How will last year’s best performing small capitalization stock manager further your financial well being if your goals and objectives dictate that you should not even consider holding ‘small-cap’ stocks in the first place? Not having determined precisely what asset allocation will best support an investor’s goals and objectives may result in investing too heavily in one asset class purely because of that investor’s familiarity with that type of asset. As we near the end of our odyssey through the IMC process, the four steps we have covered in previous issues are summarized as follows.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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 teacher head, 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".